22.2.1 Face Analysis, Evaluations, Benchmarks, Databases of Images

Chapter Contents (Back)
Face Recognition. Application, Faces. Survey, Face Recognition. Evaluation, Faces. Recognition Bias discussion:
See also Bias in Face Analysis, Evaluaions, Fairness.
See also Face Verification, Authentication, Evaluations, Verification Benchmarks.
See also Face Analysis, General Papers, Surveys.

300 Videos in the Wild,
2015 Dataset, Faces.
WWW Link. Used for the ICCV 2015 workshop challenge.

WIDER Attribute dataset,
2016.
WWW Link. Dataset, Faces.
See also Human Attribute Recognition by Deep Hierarchical Contexts.

Description of the Collection of Facial Images,
2007 Dataset, Faces.
HTML Version. Essex collection of faces. 395 people, 20 images each.

Annotated Facial Dataset,
2007 Dataset, Faces.
WWW Link.

The CMU Multi-PIE Face Database,
2010 Dataset, Faces.
WWW Link. It contains 337 subjects, captured under 15 view points and 19 illumination conditions in four recording sessions for a total of more than 750,000 images.

FaceScrub Annotated Face Dataset,
2014 Dataset, Faces.
HTML Version. 100,000 images of 530 people. Acquired from internet search with rejection of pictures that do not match.
See also data-driven approach to cleaning large face datasets, A.

GVVPerfcapEva Repository of Evaluation Data Sets,
2015 Dataset, Faces. Dataset, Human Motion. Dataset, Hand Tracking.
WWW Link. A set of dataset including:

GVVPerfCapEva: IDT - Full body skeletal motion capture results from from 
 body-worn inertial sensor data and depth camera recordings
GVVPerfCapEva: Dexter 1: Evaluation data set for 3D hand tracking with 
 depth and multi-view video data
GVVPerfCapEva: PDT 2013: Body shape estimation and real-time motion 
 capture with a depth camera
GVVPerfcapEva: BinoCap - Dense 3D full-body performance capture with 
 handheld stereo cameras (single + multiple person(s))
GVVPerfcapEva: MonFacecCap - Monocular dense face performance capture
GVVPerfCapEva: MVIC - markerless multi-view performance capture of 
 multiple interacting characters
GVVPerfCapEva: HKIC: Performance capture of interacting characters with 
 handheld Kinects

MPII Human Shape,
2015 Dataset, Human Pose.
WWW Link. Expressive 3D human body shape models and tools for human shape space building.

UB KinFace Database,
2011 Dataset, Faces.
HTML Version.

Yale Face Database,
Online2006. First is 165 images.
HTML Version. And 5760 single light source images of 10 subjects each seen under 576 viewing conditions
HTML Version. Dataset, Faces. BibRef 0600

The University of Oulu Physics-Based Face Database,
2000. 125 different faces each in 16 different camera calibration and illumination conditions.
WWW Link. Dataset, Faces.

The University of Oulu Face Video Database,
2002.
WWW Link. Dataset, Faces.

The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations,
2004. 9,594 images of 1040 individuals (595 males and 445 females) with varying Pose, Expression, Accessory, and Lighting
HTML Version. Dataset, Faces.

MIT Face Recognition Database,
Online2000 Fi Dataset, Faces.
HTML Version.
HTML Version. First one is small (19X19) images. Second one has training and test data. BibRef 0001

The UMIST Face Database,
1998. Face Recognition.
HTML Version. Dataset, Faces.

NIST Mugshot Identification Database,
2002.
HTML Version. Dataset, Faces.

IARPA Janus Benchmark A (IJB-A) dataset,
2017.
WWW Link. Dataset, Faces.

The ORL Database of Faces,
1992-1994. More recently called the AT&T database.
HTML Version. Dataset, Faces.

PubFig: Public Figures Face Database,
2015 Dataset, Faces.
WWW Link. 58,797 images of 200 people collected from the internet. Refer to:
See also Attribute and simile classifiers for face verification.

Peer, P.[Peter],
CVL Face Database,
Online1999. Dataset, Faces.
HTML Version. 114 people, 7 images each. BibRef 9900

POSTECH Face Database,
2001 Dataset, Faces. Dataset, Expressions. Dataset, Gesture.
HTML Version. A variety of datasets for face recognition, expression recognition, gesture recognition, and video surveillance.
See also POSTECH face database (PF07) and performance evaluation, The.

Face Recognition Vendor Test 2006,
Online2006.
WWW Link. Dataset, Faces.
WWW Link. Results in February 2007. BibRef 0600

FacePix Database,
Online2009.
WWW Link. Dataset, Faces. 181 poses 1 degree apart plus lighting (direction) changes.
See also Arizona State University. 0906
BibRef

YouTube Faces DB,
2015 Dataset, Faces.
WWW Link. A database of face videos designed for studying the problem of unconstrained face recognition in videos. The data set contains 3,425 videos of 1,595 different people.

Oxford Town Center,
2009 Dataset, Human Tracking.
WWW Link. Pedestrian detection and tracking.

CHUK Datasets,
2009 Dataset, Pedestrian Tracking. Dataset, Crowd Analysis. Dataset, Pedestrian Detection. Dataset, Re-Identification.
HTML Version. Person search, re-identification

A View From Somewhere (AVFS),
2023 Dataset, Face Similarity.
WWW Link. A dataset of 638,180 human judgments of face similarity.

Jain, V.[Vidit], Learned-Miller, E.G.[Erick G.],
FDDB: Face Detection Data Set and Benchmark,
UMass2010, Technical Report 2010-009.
WWW Link. Dataset, Faces. annotations for 5171 faces in a set of 2845 images. Subset of
See also Labeled faces in the wild: A database for studying face recognition in unconstrained environments. BibRef 1000

Yamaguchi, K.[Kota], Berg, A.C.[Alexander C.], Ortiz, L.E.[Luis E.], Berg, T.L.[Tamara L.],
Who are you with and where are you going?,
CVPR11(1345-1352).
IEEE DOI 1106
BibRef

Huang, G.B., Ramesh, M., Berg, T.L., Learned-Miller, E.G.,
Labeled faces in the wild: A database for studying face recognition in unconstrained environments,
UMass2007, Technical Report 07-49. annotated faces captured from news articles on the web. Dataset, Faces.
WWW Link. Detected using:
See also Robust Real-Time Face Detection. BibRef 0700

Robertson, G.[Graham], Craw, I.[Ian],
Testing Face Recognition Systems,
IVC(12), No. 9, November 1994, pp. 609-614.
Elsevier DOI BibRef 9411
Earlier: BMVC93(xx)
PDF File. 9309
Aberdeen Univ. BibRef

Phillips, P.J., Moon, H.J., Rizvi, S.A., Rauss, P.J.,
The FERET Evaluation Methodology for Face-Recognition Algorithms,
PAMI(22), No. 10, October 2000, pp. 1090-1104.
IEEE DOI Evaluation, Faces. Dataset, Faces. 0011
BibRef
Earlier: A1, A2, A4, A3: CVPR97(137-143).
IEEE DOI
PDF File. 9704
Evaluation; data. BibRef

Phillips, P.J.[P. Jonathon], Wechsler, H.[Harry], Huang, J.[Jeffery], Rauss, P.J.[Patrick J.],
The FERET Database and Evaluation Procedure for Face-Recognition Algorithms,
IVC(16), No. 5, April 27 1998, pp. 295-306.
Elsevier DOI 9805
Evaluation, Faces. Dataset, Faces. BibRef

The FERET Database,
NIST1993.
WWW Link. Dataset, Faces. Old version. For Color --
See also Color FERET Database, The.
See also National Institute of Standards and Technology (NIST) Intelligent Systems Division. BibRef 9300

The Color FERET Database,
NISTJanuary 2008.
WWW Link. Dataset, Faces. BibRef 0801

Rizvi, S.A., Phillips, P.J., Moon, H.J.,
The FERET Verification Testing Protocol for Face Recognition Algorithms,
AFGR98(48-53).
IEEE DOI
PDF File. Face Verification. BibRef 9800

Quinn, G.[George], Grother, P.J.[Patrick J.],
False Matches and Non-independence of Face Recognition Scores,
BTAS08(1-5).
IEEE DOI 0809
BibRef

Phillips, P.J., Grother, P.J., Michaels, R., Blackburn, D., Tabassi, E., and Bone, J.,
FRVT 2002: Overview and Summary,
OnlineMarch 2003.
WWW Link. BibRef 0303

Beveridge, J.R.[J. Ross], Givens, G.H.[Geof H.], Phillips, P.J.[P. Jonathon], Draper, B.A.[Bruce A.],
Factors that influence algorithm performance in the Face Recognition Grand Challenge,
CVIU(113), No. 6, June 2009, pp. 750-762.
Elsevier DOI 0904
Face recognition; Subject covariates; Performance analysis; Statistical modeling BibRef

Phillips, P.J.[P. Jonathon], Yates, A.N., Beveridge, J.R.[J. Ross], Givens, G.H.[Geof H.],
Predicting Face Recognition Performance in Unconstrained Environments,
Biometrics17(557-565)
IEEE DOI 1709
Algorithm design and analysis, Cameras, Face, Face recognition, Prediction algorithms, Videos BibRef

Bolme, D.S.[David S.], Beveridge, J.R.[J. Ross], Draper, B.A.[Bruce A.],
FaceL: Facile Face Labeling,
CVS09(21-32).
Springer DOI 0910
BibRef

Givens, G.H.[Geof H.], Beveridge, J.R.[J. Ross], Draper, B.A.[Bruce A.], Bolme, D.S.[David S.],
Using a Generalized Linear Mixed Model to Study the Configuration Space of a PCA+LDA Human Face Recognition Algorithm,
AMDO04(1-11).
Springer DOI 0505
BibRef

Givens, G.H.[Geof H.], Beveridge, J.R.[J. Ross], Draper, B.A., Phillips, P.J.[P. Jonathon],
Repeated Measures GLMM Estimation of Subject-Related and False Positive Threshold Effects on Human Face Verification Performance,
EEMCV05(III: 40-40).
IEEE DOI 0507
BibRef

Givens, G., Beveridge, J.R., Draper, B.A., Grother, P.J., Phillips, P.J.,
How features of the human face affect recognition: a statistical comparison of three face recognition algorithms,
CVPR04(II: 381-388).
IEEE DOI 0408
BibRef

Rukhin, A., Grother, P.J., Phillips, P.J., Leigh, S., Heckert, A., Newton, E.M.,
Dependence characteristics of face recognition algorithms,
ICPR02(II: 36-39).
IEEE DOI 0211
BibRef

Phillips, P.J.[P. Jonathon], Grother, P.J.[Patrick J.], Micheals, R.J.[Ross J.], Blackburn, D.M., Tabassi, E., Bone, M.,
Face recognition vendor test 2002,
AMFG03(44-44).
IEEE DOI 0311
BibRef
Earlier: A2, A3, A1, Only:
Face Recognition Vendor Test 2002 Performance Metrics,
AVBPA03(937-945).
Springer DOI 0310
BibRef

Chen, L.F.[Li-Fen], Liao, H.Y.M.[Hong-Yuan Mark], Lin, J.C.[Ja-Chen], Han, C.C.[Chin-Chuan],
Why recognition in a statistics-based face recognition system should be based on the pure face portion: a probabilistic decision-based proof,
PR(34), No. 7, July 2001, pp. 1393-1403.
Elsevier DOI 0105
BibRef

Chen, L.F.[Li-Fen], Liao, H.Y.[Hong-Yuan], Chen, L.H.[Liang-Hua], Han, C.C.[Chin-Chuan], Lin, J.C.[Ja-Chen],
Why A Statistics-Based Face Recognition System Should Base Its Recognition on the Pure Face Portion: A Probabilistic Decision-Based Proof,
ICPR98(PRP1). 9808
Not online. BibRef

Heisele, B.[Bernd], Ho, P.[Purdy], Wu, J.[Jane], Poggio, T.[Tomaso],
Face recognition: component-based versus global approaches,
CVIU(91), No. 1-2, July-August 2003, pp. 6-21.
Elsevier DOI 0309
BibRef

Heisele, B.[Bernd], Ho, P.[Purdy], Poggio, T.[Tomaso],
Face Recognition with Support Vector Machines: Global versus Component-based Approach,
ICCV01(II: 688-694).
IEEE DOI 0106
BibRef

Heisele, B.[Bernd], Riskov, I.[Ivaylo], Morgenstern, C.[Christian],
Components for Object Detection and Identification,
CLOR06(225-237).
Springer DOI 0711
BibRef

Heisele, B.[Bernd], Koshizen, T.,
Components for face recognition,
AFGR04(153-158).
IEEE DOI 0411
BibRef

Heisele, B., Verri, A., Poggio, T.,
Learning and vision machines,
PIEEE(90), No. 7, July 2002, pp. 1164-1177.
IEEE DOI 0207
BibRef

Ruiz-del-Solar, J., Navarrete, P.,
Eigenspace-based face recognition: a comparative study of different approaches,
SMC-C(35), No. 3, August 2005, pp. 315-325.
IEEE DOI 0508
BibRef
Earlier: A2, A1:
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IEEE DOI 0210
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Orozco-Alzate, M.[Mauricio], Castellanos-Domínguez, C.G.[César Germán],
Comparison of the nearest feature classifiers for face recognition,
MVA(17), No. 5, October 2006, pp. 279-285.
Springer DOI 0609
BibRef

Orozco-Alzate, M.[Mauricio], Duin, R.P.W.[Robert P. W.], Castellanos-Domínguez, C.G.[César Germán],
A generalization of dissimilarity representations using feature lines and feature planes,
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BibRef
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Springer DOI 0711
Dissimilarity; Representation; Feature lines; Feature planes; Generalization BibRef

Ruiz-Muńoz, J.F.[José Francisco], Orozco-Alzate, M.[Mauricio], Castellanos-Domínguez, C.G.[César Germán],
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Murakawa, A.[Akira],
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WWW Link. faces BibRef 0304

Beveridge, J.R.[J. Ross], Bolme, D.S.[David S.], Draper, B.A.[Bruce A.], Teixeira, M.[Marcio],
The CSU Face Identification Evaluation System: Its purpose, features, and structure,
MVA(16), No. 2, February 2005, pp. 128-138.
Springer DOI 0501
Evaluation, Faces. BibRef
Earlier: A2, A1, A4, A3: CVS03(304 ff).
Springer DOI
HTML Version. 0306
Code, Face Recognition. The four algorithms provided are principle components analysis (PCA), a.k.a eigenfaces (
See also Eigenfaces for Recognition. ), a combined principle components analysis and linear discriminant analysis algorithm (PCA + LDA) (
See also Discriminant Analysis of Principal Components for Face Recognition. ), an intrapersonal/extrapersonal image difference classifier (IIDC), (
See also Bayesian similarity measure for deformable image matching, A. ) and an elastic bunch graph matching (EBGM) algorithm (
See also Face Recognition by Elastic Bunch Graph Matching. ) The PCA + LDA, IIDC, and EBGM algorithms are based upon algorithms used in the FERET study (
See also FERET Evaluation Methodology for Face-Recognition Algorithms, The. ). BibRef

Beveridge, J.R.[J. Ross], Givens, G.H.[Geof H.], Phillips, P.J.[P. Jonathon], Draper, B.A.[Bruce A.], Bolme, D.S.[David S.], Lui, Y.M.[Yui Man],
FRVT 2006: Quo Vadis face quality,
IVC(28), No. 5, May 2010, pp. 732-743.
Elsevier DOI 1003
BibRef
Earlier: A1, A2, A3, A4, A6, Only:
Focus on quality, predicting FRVT 2006 performance,
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IEEE DOI 0809

See also Face Recognition Vendor Test 2006. Face recognition; Generalized linear mixed models; Image covariates; Biometric quality Report on FRVT evaluations. BibRef

Phillips, P.J.[P. Jonathon], Scruggs, W.T.[W. Todd], O'Toole, A.J.[Alice J.], Flynn, P.J.[Patrick J.], Bowyer, K.W.[Kevin W.], Schott, C.L.[Cathy L.], Sharpe, M.[Matthew],
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PAMI(32), No. 5, May 2010, pp. 831-846.
IEEE DOI 1003
Survey, Face Recognition. Evaluation, Face Recognition. From 2 large 2006 challange tests. In 2006, the best algorithms were better than people on unfamiliar faces. BibRef

Wang, P.[Peng], Ji, Q.A.[Qi-Ang], Wayman, J.L.[James L.],
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PAMI(29), No. 4, April 2007, pp. 665-670.
IEEE DOI 0703
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O'Toole, A.J.[Alice J.], Phillips, P.J.[P. Jonathon], Jiang, F.[Fang], Ayyad, J.H.[Janet H.], Penard, N.[Nils], Abdi, H.[Herve],
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination,
PAMI(29), No. 9, September 2007, pp. 1642-1646.
IEEE DOI 0709
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Ng, J.K.C.[Johnny K.C.], Zhong, Y.Z.[Yu-Zhuo], Yang, S.Q.[Shi-Qiang],
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Elsevier DOI 0711
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Zou, J.[Jie], Ji, Q., Nagy, G.[George],
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Mohanty, P.K.[Pranab K.], Sarkar, S.[Sudeep], Kasturi, R.[Rangachar],
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IEEE DOI 0711
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ICPR06(IV: 598-601).
IEEE DOI 0609
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IEEE DOI 0507
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Springer DOI 0806
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Face, Face recognition, Databases, Cameras, Image recognition, The tufts face database, cross-modality BibRef

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Face recognition, Deep representation learning, Long-tail distribution, Aggregate-and-disperse BibRef

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Face, Face recognition, Nickel, Noise measurement, Robustness, Search engines, Visualization BibRef

Lathuiličre, S.[Stéphane], Mesejo, P.[Pablo], Alameda-Pineda, X.[Xavier], Horaud, R.[Radu],
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Computer architecture, Task analysis, Pose estimation, Systematics, Deep learning, Benchmark testing, facial landmark detection BibRef

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Dataset, Face Recognition. Biometrics, Disguise recognition, Face database, Face recognition, Multi-modal BibRef

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Face quality assessment, Hashing, Convolution neural network, Face recognition BibRef

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IEEE DOI 2201
[Applications Corner] Social networking (online), User-generated content, Medical services, Streaming media, Media, Real-time systems, Facial recognition BibRef

Marwa, K.[Kebir], Kais, O.[Ouni],
Perspectives Of Classical Methods Of Face Recognition,
ISCV22(1-8)
IEEE DOI 2208
Training, Support vector machines, Dimensionality reduction, Deep learning, Image recognition, Databases, Face recognition, SVM BibRef

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CVPR21(10487-10497)
IEEE DOI 2111
Face recognition, Benchmark testing, Training, Protocols, Training data, Standards, Internet, Large-scale face recognition, biometric authentication. Training, Knowledge engineering, Protocols, Face recognition, Pipelines, Training data, Benchmark testing BibRef

Fan, S.J.[Shao-Jing], Shen, Z.[Zhiqi], Koenig, B.L.[Bryan L.], Ng, T.T.[Tian-Tsong], Kankanhalli, M.S.[Mohan S.],
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IEEE DOI 2303
Videos, Optical imaging, Observers, Entropy, Visualization, Face recognition, Emotion recognition, Emotions, deep neural network BibRef

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Face recognition, Synthetic data, Biometrics BibRef

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Surveys(56), No. 3, October 2023, pp. xx-yy.
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Survey, Facial Expressions. pose variation, deep learning, local feature, facial occlusion, facial expression, 3D face recognition BibRef

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Neural networks, Biometrics, Classification, Face recognition, Open-set, Watchlist BibRef


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GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations,
AMFG23(3078-3087)
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Consensus Ranking for Efficient Face Image Retrieval: A Novel Method for Maximising Precision and Recall,
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Bai, X.Y.[Xia-Ying], Zheng, W.X.[Wen-Xian], Yang, W.M.[Wen-Ming], Wang, G.J.[Gui-Jin], Liao, Q.M.[Qing-Min],
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ICIP23(3513-3517)
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Wu, H.[Haiyu], Bezold, G.[Grace], Günther, M.[Manuel], Boult, T.[Terrance], King, M.C.[Michael C.], Bowyer, K.W.[Kevin W.],
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Pérez, J.C.[Juan C.], Alfarra, M.[Motasem], Thabet, A.[Ali], Arbeláez, P.[Pablo], Ghanem, B.[Bernard],
Towards Characterizing the Semantic Robustness of Face Recognition,
LXCV23(315-325)
IEEE DOI 2309
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Chen, M.Q.[Ming-Qiang], Liu, L.[Lizhe], Chen, X.[Xiaohao], Zhu, S.[Siyu],
GB-Cosface: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification,
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Sankaran, N.[Nishant], Mohan, D.D.[Deen Dayal], Tulyakov, S.[Sergey], Setlur, S.[Srirangaraj], Govindaraju, V.[Venugopal],
TADPool: Target Adaptive Pooling for Set Based Face Recognition,
FG21(1-8)
IEEE DOI 2303
Q-factor, Adaptive systems, Face recognition, Focusing, Gesture recognition, Mirrors BibRef

Knoche, M.[Martin], Hormann, S.[Stefan], Rigoll, G.[Gerhard],
Cross-Quality LFW: A Database for Analyzing Cross- Resolution Image Face Recognition in Unconstrained Environments,
FG21(1-5)
IEEE DOI 2303
Image quality, Protocols, Image resolution, Databases, Face recognition, Benchmark testing, Predictive models BibRef

Shen, B.[Bingyu], RichardWebster, B.[Brandon], O'Toole, A.[Alice], Bowyer, K.W.[Kevin W.], Scheirer, W.J.[Walter J.],
A Study of the Human Perception of Synthetic Faces,
FG21(1-8)
IEEE DOI 2303
Face recognition, Psychology, Gesture recognition, Observers BibRef

Franc, V.[Vojtech], Yermakov, A.[Andrii],
Dominant subject recognition by Bayesian learning,
FG21(01-05)
IEEE DOI 2303
Faces which appear most frequently in a collection. Image recognition, Protocols, Image databases, Face recognition, Prediction algorithms BibRef

Hernandez-Ortega, J.[Javier], Fierrez, J.[Julian], Serna, I.[Ignacio], Morales, A.[Aythami],
FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment,
FG21(1-8)
IEEE DOI 2303
Training, Q-factor, Databases, Face recognition, Sociology, Estimation, Generative adversarial networks BibRef

Qu, T.Y.[Ting-Yu], Tuytelaars, T.[Tinne], Moens, M.F.[Marie-Francine],
Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive Loss,
WACV23(3494-3503)
IEEE DOI 2302
Training, Adaptation models, Uncertainty, Computational modeling, Neural networks, Cognition BibRef

Boutros, F.[Fadi], Grebe, J.H.[Jonas Henry], Kuijper, A.[Arjan], Damer, N.[Naser],
IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models,
ICCV23(19593-19604)
IEEE DOI 2401
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Terhörst, P.[Philipp], Ihlefeld, M.[Malte], Huber, M.[Marco], Damer, N.[Naser], Kirchbuchner, F.[Florian], Raja, K.[Kiran], Kuijper, A.[Arjan],
QMagFace: Simple and Accurate Quality-Aware Face Recognition,
WACV23(3473-3483)
IEEE DOI 2302
Training, Image quality, Image recognition, Databases, Face recognition, Computational modeling, Algorithms: Biometrics BibRef

Bae, G.[Gwangbin], de la Gorce, M.[Martin], Baltrušaitis, T.[Tadas], Hewitt, C.[Charlie], Chen, D.[Dong], Valentin, J.[Julien], Cipolla, R.[Roberto], Shen, J.J.[Jing-Jing],
DigiFace-1M: 1 Million Digital Face Images for Face Recognition,
WACV23(3515-3524)
IEEE DOI 2302
Ethics, Error analysis, Face recognition, Computational modeling, Pipelines, Lighting, Algorithms: Biometrics, face, gesture, body pose, visual reasoning BibRef

Zhang, M.Y.[Man-Yuan], Song, G.L.[Guang-Lu], Liu, Y.[Yu], Li, H.S.[Hong-Sheng],
Towards Robust Face Recognition with Comprehensive Search,
ECCV22(XII:720-736).
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Wu, C.Y.[Cho-Ying], Hsu, C.C.[Chin-Cheng], Neumann, U.[Ulrich],
Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?,
CVPR22(10442-10451)
IEEE DOI 2210
Geometry, Visualization, Correlation, Numerical analysis, Face recognition, Vision + X, Biometrics, Explainable computer vision BibRef

Kim, M.C.[Min-Chul], Jain, A.K.[Anil K.], Liu, X.M.[Xiao-Ming],
AdaFace: Quality Adaptive Margin for Face Recognition,
CVPR22(18729-18738)
IEEE DOI 2210
Image quality, Training, Adaptation models, Codes, Face recognition, Training data, Face and gestures, Recognition: detection, retrieval BibRef

Voo, K.T.R.[Kenny T. R.], Jiang, L.M.[Li-Ming], Loy, C.C.[Chen Change],
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets,
VDU22(4710-4719)
IEEE DOI 2210
Codes, Annotations, Face recognition, Benchmark testing, Robustness BibRef

Robbins, W.[Wes], Boult, T.E.[Terrance E.],
On the Effect of Atmospheric Turbulence in the Feature Space of Deep Face Recognition,
Biometrics22(1617-1625)
IEEE DOI 2210
Image quality, Extraterrestrial phenomena, Face recognition, Atmospheric modeling, Computational modeling BibRef

Kornilova, A.[Anastasiia], Faizullin, M.[Marsel], Pakulev, K.[Konstantin], Sadkov, A.[Andrey], Kukushkin, D.[Denis], Akhmetyanov, A.[Azat], Akhtyamov, T.[Timur], Taherinejad, H.[Hekmat], Ferrer, G.[Gonzalo],
SmartPortraits: Depth Powered Handheld Smartphone Dataset of Human Portraits for State Estimation, Reconstruction and Synthesis,
CVPR22(21286-21297)
IEEE DOI 2210
Video sequences, Cameras, Sensor systems and applications, Software, Trajectory, Synchronization, Datasets and evaluation, RGBD sensors and analytics BibRef

Fu, B.Y.[Bi-Ying], Damer, N.[Naser],
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models,
Explain-Bio22(349-358)
IEEE DOI 2202
Image quality, Degradation, Analytical models, Visualization, Limiting, Face recognition, Conferences BibRef

Luo, J.H.[Jia-Hao], Khan, F.[Fahim], Mori, I.[Issei], de Silva, A.[Akila], Ruezga, E.[Eric], Davis, J.[James],
Face Models: How Good Does My Data Need To Be?,
ICIP21(3188-3192)
IEEE DOI 2201
Solid modeling, Reconstruction algorithms, Laser modes, Data models, Faces, Imaging, 3D, Face Models BibRef

Wang, N.X.[Nan-Xi], Wang, Z.Y.[Zhong-Yuan], He, Z.[Zheng], Huang, B.J.[Bao-Jin], Zhou, L.G.[Li-Guo], Han, Z.[Zhen],
A Tilt-Angle Face Dataset and Its Validation,
ICIP21(894-898)
IEEE DOI 2201
Training, Deep learning, Solid modeling, Image recognition, Face recognition, Video surveillance, Face dataset, overhead view, face recognition BibRef

Li, P.Y.[Peng-Yu], Wang, B.[Biao], Zhang, L.[Lei],
Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset with Limited Computational Resources,
CVPR21(13310-13319)
IEEE DOI 2111
Training, Measurement, Codes, Face recognition, Memory management, Graphics processing units BibRef

Liang, J.[Jie], Zeng, H.[Hui], Cui, M.M.[Miao-Miao], Xie, X.S.[Xuan-Song], Zhang, L.[Lei],
PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency,
CVPR21(653-661)
IEEE DOI 2111
Photography, Visualization, Codes, Computational modeling, Pattern recognition, Task analysis BibRef

Xu, X.K.[Xing-Kun], Huang, Y.[Yuge], Shen, P.C.[Peng-Cheng], Li, S.X.[Shao-Xin], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue], Li, Y.[Yong], Cui, Z.[Zhen],
Consistent Instance False Positive Improves Fairness in Face Recognition,
CVPR21(578-586)
IEEE DOI 2111
Training, Codes, Annotations, Face recognition, Benchmark testing BibRef

Tong, L.[Liang], Chen, Z.Z.[Zheng-Zhang], Ni, J.C.[Jing-Chao], Cheng, W.[Wei], Song, D.J.[Dong-Jin], Chen, H.F.[Hai-Feng], Vorobeychik, Y.[Yevgeniy],
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems,
CVPR21(13249-13258)
IEEE DOI 2111
Systematics, Face recognition, Perturbation methods, Neural networks, Training data, Computer architecture BibRef

Poster, D.[Domenick], Thielke, M.[Matthew], Nguyen, R.[Robert], Rajaraman, S.[Srinivasan], Di, X.[Xing], Fondje, C.N.[Cedric Nimpa], Patel, V.M.[Vishal M.], Short, N.J.[Nathaniel J.], Riggan, B.S.[Benjamin S.], Nasrabadi, N.M.[Nasser M.], Hu, S.[Shuowen],
A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset,
WACV21(1558-1567)
IEEE DOI
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Dataset, Face Recognition. Heating systems, Protocols, Thermal lensing, Photothermal effects, Cameras, Thermal analysis, Task analysis BibRef

Balazia, M.[Michal], Happy, S.L.[S L], Brémond, F.[François], Dantcheva, A.[Antitza],
How Unique Is a Face: An Investigative Study,
ICPR21(7066-7071)
IEEE DOI 2105
Image resolution, Protocols, Databases, Face recognition, Banking, Feature extraction, Entropy BibRef

Chen, K.[Ken], Wu, Y.C.[Yi-Chao], Li, Z.M.[Zhen-Mao], Wu, Y.D.[Yu-Dong], Liang, D.[Ding],
Face Image Quality Assessment for Model and Human Perception,
ICPR21(3003-3010)
IEEE DOI 2105
Training, Image quality, Adaptation models, Systematics, Target recognition, Face recognition, Benchmark testing BibRef

Greco, A.[Antonio], Saggese, A.[Alessia], Vento, M.[Mario], Vigilante, V.[Vincenzo],
Performance Assessment of Face Analysis Algorithms with Occluded Faces,
DEEPRETAIL20(472-486).
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Guo, Z.,
The development and comparison of face recognition algorithms based on different technical characteristics,
CVIDL20(6-10)
IEEE DOI 2102
convolutional neural nets, face recognition, image matching, human face, YOLO BibRef

Jiang, E.,
A review of the comparative studies on traditional and intelligent face recognition methods,
CVIDL20(11-15)
IEEE DOI 2102
face recognition, feature extraction, image representation, learning (artificial intelligence), statistical analysis, Deep learning BibRef

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Explainable Face Recognition,
ECCV20(XI:248-263).
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Explain why the match was returned. BibRef

Wang, P., Wang, Z., Ji, Z., Liu, X., Yang, S., Wu, Z.,
TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning,
EmotioNet20(1653-1656)
IEEE DOI 2008
Training, Noise measurement, Face recognition, Face, Data models, Optimization BibRef

Yang, H., Zhu, H., Wang, Y., Huang, M., Shen, Q., Yang, R., Cao, X.,
FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction,
CVPR20(598-607)
IEEE DOI 2008
Face, Solid modeling, Shape, Geometry, Predictive models, Cameras BibRef

Albiero, V.[Vítor], Krishnapriya, K.S., Vangara, K.[Kushal], Zhang, K.[Kai], King, M.C.[Michael C.], Bowyer, K.W.[Kevin W.],
Analysis of Gender Inequality In Face Recognition Accuracy,
WACVWS20(81-89)
IEEE DOI 2006
Face recognition, Face, Machine learning, Forehead, Training data, Databases BibRef

Bruveris, M., Mortazavian, P., Gietema, J., Mahadevan, M.,
Reducing Geographic Performance Differentials for Face Recognition,
WACVWS20(98-106)
IEEE DOI 2006
Face recognition, Training, Data models, Face, Training data, Security, Data mining BibRef

Deng, J., Guo, J., Zhang, D., Deng, Y., Lu, X., Shi, S.,
Lightweight Face Recognition Challenge,
LFR19(2638-2646)
IEEE DOI 2004
convolutional neural nets, face recognition, image sensors, large-scale datasets, extensive comparison metric, lightweight face recognition BibRef

Bagrov, N.U., Konushin, A.S., Konushin, V.S.,
Face Recognition With Low False Positive Error Rate,
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Xu, X., Kakadiaris, I.A.,
FaRE: Open Source Face Recognition Performance Evaluation Package,
ICIP19(3272-3276)
IEEE DOI 1910
Code, Face Recognition. Face Recognition, Evaluation, Toolbox BibRef

Dhar, P.[Prithviraj], Castillo, C.[Carlos], Chellappa, R.[Rama],
On Measuring the Iconicity of a Face,
WACV19(2137-2145)
IEEE DOI 1904
For a given face, some in the database are more representative that the others. face recognition, feature extraction, multilayer perceptrons, face dataset, iconic images, face image, iconicity scores, Task analysis BibRef

Gallo, I., Nawaz, S., Calefati, A., Piccoli, G.,
A Pipeline to Improve Face Recognition Datasets and Applications,
IVCNZ18(1-6)
IEEE DOI 1902
Videos, Face, Pipelines, Face recognition, Clustering algorithms, Cleaning, YouTube, face recognition, convolutional neural network, cleaning dataset BibRef

Akbir, K., Mahmoud, M.,
Considering Race a Problem of Transfer Learning,
DVPBA19(100-106)
IEEE DOI 1902
Task analysis, Image generation, Hair, Benchmark testing, Biological system modeling, Training BibRef

Nguyen, V., Tran, M., Luo, J.,
Are French Really That Different? Recognizing Europeans from Faces Using Data-Driven Learning,
ICPR18(2729-2734)
IEEE DOI 1812
Facial features, Twitter, Europe, Neural networks, Sociology, Statistics, Training BibRef

Ferrari, C., Berretti, S.[Stefano], del Bimbo, A.[Alberto],
Extended YouTube Faces: A Dataset for Heterogeneous Open-Set Face Identification,
ICPR18(3408-3413)
IEEE DOI 1812
Face, Protocols, Face recognition, Videos, Probes, Benchmark testing, Media
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Zhong, Y., Deng, W.,
Deep Difference Analysis in Similar-looking Face recognition,
ICPR18(3353-3358)
IEEE DOI 1812
Face, Heating systems, Visualization, Face recognition, Feature extraction, Databases, Task analysis BibRef

Martínez-Díaz, Y., Méndez-Vázquez, H., López-Avila, L., Chang, L., Sucar, L.E., Tistarelli, M.[Massimo],
Toward More Realistic Face Recognition Evaluation Protocols for the YouTube Faces Database,
Biometrics18(526-5268)
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Videos, Face, Protocols, Databases, Face recognition, Measurement, Standards
See also YouTube Faces DB. BibRef

de Castro, D.C.[Daniel Coelho], Nowozin, S.[Sebastian],
From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data,
ECCV18(II: 764-780).
Springer DOI 1810
BibRef

Xie, W.D.[Wei-Di], Shen, L.[Li], Zisserman, A.[Andrew],
Comparator Networks,
ECCV18(XI: 811-826).
Springer DOI 1810
decide if two sets of images of a face are of the same person or not. BibRef

Wang, F.[Fei], Chen, L.[Liren], Li, C.[Cheng], Huang, S.[Shiyao], Chen, Y.J.[Yan-Jie], Qian, C.[Chen], Loy, C.C.[Chen Change],
The Devil of Face Recognition Is in the Noise,
ECCV18(IX: 780-795).
Springer DOI 1810
BibRef

Liu, X.F.[Xiao-Feng], Vijaya Kumar, B.V.K., Yang, C.[Chao], Tang, Q.M.[Qing-Ming], You, J.[Jane],
Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets,
ECCV18(XI: 573-590).
Springer DOI 1810
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Cao, J., Li, Y., Zhang, Z.,
Celeb-500K: A Large Training Dataset for Face Recognition,
ICIP18(2406-2410)
IEEE DOI 1809
Dataset, Face Recognition. Training, Face, Face recognition, Measurement, Learning systems, Performance gain, Face detection, face recognition, face dataset, convolutional neural networks BibRef

Peng, B., Yang, H., Li, D., Zhang, Z.,
An Empirical Study of Face Recognition under Variations,
FG18(310-317)
IEEE DOI 1806
Distortion, Face, Feature extraction, Image coding, Lighting, Deep learning, Face Recognition, Handcrafted feature BibRef

Banerjee, S., Brogan, J., Krizaj, J., Bharati, A., Webster, B.R., Struc, V., Flynn, P.J., Scheirer, W.J.,
To Frontalize or Not to Frontalize: Do We Really Need Elaborate Pre-processing to Improve Face Recognition?,
WACV18(20-29)
IEEE DOI 1806
face recognition, feedforward neural nets, learning (artificial intelligence), CNNs, BibRef

Nech, A., Kemelmacher-Shlizerman, I.,
Level Playing Field for Million Scale Face Recognition,
CVPR17(3406-3415)
IEEE DOI 1711
Benchmark testing, Clustering algorithms, Face, Face recognition, Flickr, Labeling, Training BibRef

Whitelam, C., Taborsky, E., Blanton, A., Maze, B., Adams, J., Miller, T., Kalka, N., Jain, A.K., Duncan, J.A., Allen, K., Cheney, J., Grother, P.,
IARPA Janus Benchmark-B Face Dataset,
Biometrics17(592-600)
IEEE DOI 1709
Dataset, Faces. Benchmark testing, Face, Face detection, Face recognition, Media, Protocols, Videos
See also IARPA Janus Benchmark A (IJB-A) dataset. BibRef

Hassanpour, N.[Negar], Chen, L.[Liang],
A Quantum Probability Inspired Framework for Image-Set Based Face Identification,
FG17(551-557)
IEEE DOI 1707
Face, Hilbert space, Quantum computing, Quantum mechanics, Silicon, Uncertainty BibRef

Phillips, P.J.[P. Jonathon],
A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition,
FG17(705-710)
IEEE DOI 1707
Algorithm design and analysis, Benchmark testing, Face, Face recognition, Lighting, NIST, Partitioning, algorithms BibRef

Zuo, H., Wang, L., Qin, J.,
XJU1: A Chinese Ethnic Minorities Face Database,
CMVIT17(7-11)
IEEE DOI 1704
face recognition BibRef

Gilani, S.Z.[Syed Zulqarnain], Mian, A.S.[Ajmal S.],
Towards Large-Scale 3D Face Recognition,
DICTA16(1-8)
IEEE DOI 1701
Databases. Recognition rates drop as size of dataset increases. BibRef

Gorodnichy, D.O.[Dmitry O.], Bissessar, D.[David], Granger, E.[Eric], Laganiére, R.[Robert],
Recognizing People and Their Activities in Surveillance Video: Technology State of Readiness and Roadmap,
CRV16(250-259)
IEEE DOI 1612
Face BibRef

Escalera, S., Torres, M.T., Martínez, B., Baró, X., Escalante, H.J., Guyon, I., Tzimiropoulos, G.[Georgios], Corneanu, C.A., Oliu Simón, M.[Marc], Bagheri, M.A., Valstar, M.,
ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016,
ChaLearn16(706-713)
IEEE DOI 1612
BibRef

de Freitas Pereira, T.[Tiago], Marcel, S.[Sebastien],
Heterogeneous Face Recognition Using Inter-Session Variability Modelling,
Biometrics16(179-186)
IEEE DOI 1612
BibRef

Adams, J.C., Allen, K.C., Miller, T., Kalka, N.D.[Nathan D.], Jain, A.K.,
Grouper: Optimizing Crowdsourced Face Annotations,
Biometrics16(163-170)
IEEE DOI 1612
BibRef

Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.,
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,
CVPR16(4873-4882)
IEEE DOI 1612
Dataset, Face Recognition. BibRef

Zhan, X.H.[Xiao-Hang], Pan, X.G.[Xin-Gang], Liu, Z.W.[Zi-Wei], Lin, D.[Dahua], Loy, C.C.[Chen Change],
Self-Supervised Learning via Conditional Motion Propagation,
CVPR19(1881-1889).
IEEE DOI 2002
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Zhan, X.H.[Xiao-Hang], Liu, Z.W.[Zi-Wei], Yan, J.J.[Jun-Jie], Lin, D.H.[Da-Hua], Loy, C.C.[Chen Change],
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,
ECCV18(IX: 576-592).
Springer DOI 1810
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Li, Y.N.[Yi-Ning], Huang, C.[Chen], Loy, C.C.[Chen Change], Tang, X.[Xiaoou],
Human Attribute Recognition by Deep Hierarchical Contexts,
ECCV16(VI: 684-700).
Springer DOI 1611

See also WIDER Attribute dataset. BibRef

Guo, Y.D.[Yan-Dong], Zhang, L.[Lei], Hu, Y.X.[Yu-Xiao], He, X.D.[Xiao-Dong], Gao, J.F.[Jian-Feng],
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,
ECCV16(III: 87-102).
Springer DOI 1611
Dataset, Face Recognition.
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Fedorovf, I.[Igor], Giri, R.[Ritwik], Rao, B.D.[Bhaskar D.], Nguyen, T.Q.[Truong Q.],
Robust Bayesian method for simultaneous block sparse signal recovery with applications to face recognition,
ICIP16(3872-3876)
IEEE DOI 1610
Bayes methods BibRef

Iliadis, M.[Michael], Spinoulas, L.[Leonidas], Berahas, A.S.[Albert S.], Wang, H.H.[Hao-Hong], Katsaggelos, A.K.[Aggelos K.],
Multi-model robust error correction for face recognition,
ICIP16(3229-3233)
IEEE DOI 1610
Error correction BibRef

Yadav, D., Kohli, N., Pandey, P., Singh, R., Vatsa, M., Noore, A.,
Effect of illicit drug abuse on face recognition,
WACV16(1-7)
IEEE DOI 1606
Databases BibRef

Ginosar, S., Rakelly, K., Sachs, S., Yin, B., Efros, A.A.[Alexei A.],
A Century of Portraits: A Visual Historical Record of American High School Yearbooks,
Extreme15(652-658)
IEEE DOI 1602
Cameras BibRef

Herrmann, C.[Christian], Qu, C.C.[Cheng-Chao], Willersinn, D.[Dieter], Beyerer, J.[Jurgen],
Impact of resolution and image quality on video face analysis,
AVSS15(1-6)
IEEE DOI 1511
Degradation BibRef

Juefei-Xu, F.[Felix], Pal, D.K.[Dipan K.], Singh, K.[Karanhaar], Savvides, M.[Marios],
A preliminary investigation on the sensitivity of COTS face recognition systems to forensic analyst-style face processing for occlusions,
Biometrics15(25-33)
IEEE DOI 1510
Databases BibRef

Klare, B.F.[Brendan F.], Klein, B.[Ben], Taborsky, E.[Emma], Blanton, A.[Austin], Cheney, J.[Jordan], Allen, K.[Kristen], Grother, P.[Patrick], Mah, A.[Alan], Burge, M.[Mark], Jain, A.K.[Anil K.],
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A,
CVPR15(1931-1939)
IEEE DOI 1510
Dataset, Face Recognition. BibRef

Schumacher, M.[Matthaeus], Blanz, V.[Volker],
Exploration of the correlations of attributes and features in faces,
FG15(1-8)
IEEE DOI 1508
correlation methods BibRef

Beveridge, J.R., Zhang, H.[Hao], Draper, B.A., Flynn, P.J., Feng, Z.H.[Zhen-Hua], Huber, P., Kittler, J.V.[Josef V.], Huang, Z.W.[Zhi-Wu], Li, S.X.[Shao-Xin], Li, Y.[Yan], Kan, M.[Meina], Wang, R.P.[Rui-Ping], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin], Li, H.X.[Hao-Xiang], Hua, G.[Gang], Struc, V., Krizaj, J., Ding, C.X.[Chang-Xing], Tao, D.C.[Da-Cheng], Phillips, P.J.,
Report on the FG 2015 Video Person Recognition Evaluation,
FG15(1-8)
IEEE DOI 1508
face recognition BibRef

Mandal, B.[Bappaditya], Zhikai, W.[Wang], Li, L.Y.[Li-Yuan], Kassim, A.A.[Ashraf A.],
Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification,
RoLoD14(585-599).
Springer DOI 1504
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Escalera, S.[Sergio], Baró, X.[Xavier], Gonzŕlez, J.[Jordi], Bautista, M.A.[Miguel A.], Madadi, M.[Meysam], Reyes, M.[Miguel], Ponce-López, V.[Víctor], Escalante, H.J.[Hugo J.], Shotton, J.[Jamie], Guyon, I.[Isabelle],
ChaLearn Looking at People Challenge 2014: Dataset and Results,
ChaLearn14(459-473).
Springer DOI 1504
BibRef

Escalera, S.[Sergio], Fabian, J., Pardo, P., Baró, X.[Xavier], Gonzŕlez, J.[Jordi], Escalante, H.J.[Hugo J.], Misevic, D., Steiner, U., Guyon, I.[Isabelle],
ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results,
ChaLearnDec15(243-251)
IEEE DOI 1602
Cultural differences BibRef

Ekenel, H.K.,
Benchmarking Facial Image Analysis Technologies (BeFIT),
IPTA12(15-15)
IEEE DOI 1503
face recognition BibRef

Melle, A.[Andrea], Dugelay, J.L.[Jean-Luc],
Scrambling faces for privacy protection using background self-similarities,
ICIP14(6046-6050)
IEEE DOI 1502
Encryption BibRef

Ng, H.W.[Hong-Wei], Winkler, S.[Stefan],
A data-driven approach to cleaning large face datasets,
ICIP14(343-347)
IEEE DOI
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See also FaceScrub Annotated Face Dataset. BibRef

McDuff, D.J.[Daniel J.], el Kaliouby, R.[Rana], Senechal, T.[Thibaud], Amr, M.[May], Cohn, J.F.[Jeffrey F.], Picard, R.W.[Rosalind W.],
Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected 'In-the-Wild',
AMFG13(881-888)
IEEE DOI 1309
Dataset, Facial Expressions. Facial expressions;dataset BibRef

Toderici, G.[George], Evangelopoulos, G.[Georgios], Fang, T.H.[Tian-Hong], Theoharis, T.[Theoharis], Kakadiaris, I.A.[Ioannis A.],
UHDB11 Database for 3D-2D Face Recognition,
PSIVT13(73-86).
Springer DOI 1402
Dataset, Faces. BibRef

Liu, X.[Xin], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Face Recognition after Plastic Surgery: A Comprehensive Study,
ACCV12(II:565-576).
Springer DOI 1304
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Günther, M.[Manuel], Wallace, R.[Roy], Marcel, S.[Sébastien],
An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms,
BeFIT12(III: 547-556).
Springer DOI 1210
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Choi, J.H.[Jong-Hyun], Sharma, A.[Abhishek], Jacobs, D.W.[David W.], Davis, L.S.[Larry S.],
Data insufficiency in sketch versus photo face recognition,
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IEEE DOI 1207
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Lui, Y.M.[Yui Man], Bolme, D.[David], Phillips, P.J.[P. Jonathon], Beveridge, J.R.[J. Ross], Draper, B.A.[Bruce A.],
Preliminary studies on the Good, the Bad, and the Ugly face recognition challenge problem,
Biometrics12(9-16).
IEEE DOI 1207
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Colombo, A.[Alessandro], Cusano, C.[Claudio], Schettini, R.[Raimondo],
UMB-DB: A database of partially occluded 3D faces,
BenchFace11(2113-2119).
IEEE DOI 1201
Dataset, Faces. BibRef

Somanath, G.[Gowri], Rohith, M.V., Kambhamettu, C.[Chandra],
VADANA: A dense dataset for facial image analysis,
BenchFace11(2175-2182).
IEEE DOI 1201
Dataset, Faces. BibRef

Özcan, M.[Mert], Jie, L.[Luo], Ferrari, V.[Vittorio], Caputo, B.[Barbara],
A Large-Scale Database of Images and Captions for Automatic Face Naming,
BMVC11(xx-yy).
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Shi, Q.F.[Qin-Feng], Eriksson, A.[Anders], van den Hengel, A.J.[Anton J.], Shen, C.H.[Chun-Hua],
Is face recognition really a Compressive Sensing problem?,
CVPR11(553-560).
IEEE DOI 1106
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Aggarwal, G.[Gaurav], Biswas, S.[Soma], Flynn, P.J.[Patrick J.], Bowyer, K.W.[Kevin W.],
Predicting good, bad and ugly match Pairs,
WACV12(153-160).
IEEE DOI 1203
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And:
Predicting performance of face recognition systems: An image characterization approach,
Biometrics11(52-59).
IEEE DOI 1106

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Zhang, Y.[Yong], Ellyson, S.[Steve], Zone, A.[Anthony], Gangam, P.[Priyanka], Sullins, J.[John], McCullough, C.[Christine], Canavan, S.[Shaun], Yin, L.J.[Li-Jun],
Recognizing face sketches by a large number of human subjects: A perception-based study for facial distinctiveness,
FG11(707-712).
IEEE DOI 1103
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Beveridge, J.R.[J. Ross], Phillips, P.J.[P. Jonathon], Givens, G.H.[Geof H.], Draper, B.A.[Bruce A.], Teli, M.N.[Mohammad Nayeem], Bolme, D.S.[David S.],
When high-quality face images match poorly,
FG11(572-578).
IEEE DOI 1103
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Ni, J.[Jie], Chellappa, R.[Rama],
Evaluation of state-of-the-art algorithms for remote face recognition,
ICIP10(1581-1584).
IEEE DOI 1009
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Kroon, B.[Bart], Hanjalic, A.[Alan], Boughorbel, S.[Sabri],
Comparison of face matching techniques under pose variation,
CIVR07(272-279).
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Iyer, V.N., Kirkbride, S.R., Parks, B.C., Scheirer, W.J., Boult, T.E.,
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AMFG10(63-70).
IEEE DOI 1006
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Degtyarev, N.[Nikolay], Seredin, O.[Oleg],
Comparative Testing of Face Detection Algorithms,
ICISP10(200-209).
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Gupta, S.[Shalini], Castleman, K.R.[Kenneth R.], Markey, M.K.[Mia K.], Bovik, A.C.[Alan C.],
Texas 3D Face Recognition Database,
Southwest10(97-100).
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Dataset, Faces. BibRef

Kapoor, A.[Ashish], Baker, S.[Simon], Basu, S.[Sumit], Horvitz, E.[Eric],
Memory constrained face recognition,
CVPR12(2539-2546).
IEEE DOI 1208
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Kapoor, A.[Ashish], Hua, G.[Gang], Akbarzadeh, A.[Amir], Baker, S.[Simon],
Which Faces to Tag: Adding Prior Constraints into Active Learning,
ICCV09(1058-1065).
IEEE DOI
PDF File. 0909
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Ferrara, M.[Matteo], Franco, A.[Annalisa],
On the Impact of Alterations on Face Photo Recognition Accuracy,
CIAP13(I:743-751).
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Maltoni, D.[Davide], Franco, A.[Annalisa], Ferrara, M.[Matteo], Maio, D.[Dario], Nardelli, A.[Antonio],
BioLab-ICAO: A new benchmark to evaluate applications assessing face image compliance to ISO/IEC 19794-5 standard,
ICIP09(41-44).
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Giorgi, D., Attene, M., Patane, G., Marini, S., Pizzi, C., Biasotti, S., Spagnuolo, M., Falcidieno, B., Corvi, M., Usai, L., Roncarolo, L., Garibotto, G.,
A Critical Assessment of 2D and 3D Face Recognition Algorithms,
AVSBS09(79-84).
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Rakshit, R.D.[Rinku Datta], Kisku, D.R.[Dakshina Ranjan], Tistarelli, M.[Massimo], Gupta, P.[Phalguni],
Face Identification Using Local Ternary Tree Pattern Based Spatial Structural Components,
IbPRIA19(II:50-63).
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Kisku, D.R.[Dakshina R.], Tistarelli, M.[Massimo], Sing, J.K.[Jamuna Kanta], Gupta, P.[Phalguni],
Face recognition by fusion of local and global matching scores using DS theory: An evaluation with uni-classifier and multi-classifier paradigm,
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Martínez-Contreras, F.[Francisco], Orrite-Uruńuela, C.[Carlos], Martínez-del-Rincón, J.[Jesús],
AdaBoost Multiple Feature Selection and Combination for Face Recognition,
IbPRIA09(338-345).
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Franco, A.[Annalisa], Maio, D.[Dario], Maltoni, D.[Davide],
The Big Brother Database: Evaluating Face Recognition in Smart Home Environments,
ICB09(142-150).
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Poh, N.[Norman], Chan, C.H.[Chi Ho], Kittler, J.V.[Josef V.], Marcel, S.[Sébastien], McCool, C.[Christopher], Rúa, E.A.[Enrique Argones], Castro, J.L.A.[José Luis Alba], Villegas, M.[Mauricio], Paredes, R.[Roberto], Štruc, V.[Vitomir], Pavešic, N.[Nikola], Salah, A.A.[Albert Ali], Fang, H.[Hui], Costen, N.P.[Nicholas P.],
Face Video Competition,
ICB09(715-724).
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O'Toole, A.J.[Alice J.], Phillips, P.J.[P. Jonathon],
Five Principles for Crowd-Source Experiments in Face Recognition,
FG17(735-741)
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Benchmark testing, Face, Face recognition, Labeling, Machine learning, Machine, learning, algorithms BibRef

O'Toole, A.J.[Alice J.], Phillips, P.J.[P. Jonathon], Narvekar, A.[Abhijit],
Humans versus algorithms: Comparisons from the Face Recognition Vendor Test 2006,
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Kim, Y.H.[Yong-Hyun], Park, W.[Wonpyo], Shin, J.J.[Jong-Ju],
Broadface: Looking at Tens of Thousands of People at once for Face Recognition,
ECCV20(IX:536-552).
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Lee, H.S.[Hyoung-Soo], Park, S.S.[Sung-Soo], Kang, B.N.[Bong-Nam], Shin, J.J.[Jong-Ju], Lee, J.Y.[Ju-Young], Je, H.M.[Hong-Mo], Jun, B.J.[Bong-Jin], Kim, D.J.[Dai-Jin],
The POSTECH face database (PF07) and performance evaluation,
FG08(1-6).
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Huang, G.B.[Gary B.], Jones, M.J.[Michael J.], Learned-Miller, E.G.[Erik G.],
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Bastanfard, A.[Azam], Nik, M.A.[Melika Abbasian], Dehshibi, M.M.[Mohammad Mahdi],
Iranian Face Database with age, pose and expression,
ICMV07(50-55).
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Ramanan, D.[Deva], Baker, S.[Simon], Kakade, S.[Sham],
Leveraging archival video for building face datasets,
ICCV07(1-8).
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Gao, X.F.[Xiu-Feng], Li, S.Z.[Stan Z.], Liu, R.[Rong], Zhang, P.R.[Pei-Ren],
Standardization of Face Image Sample Quality,
ICB07(242-251).
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Müller, M.K.[Marco K.], Heinrichs, A.[Alexander], Tewes, A.H.J.[Andreas H. J.], Schäfer, A.[Achim], Würtz, R.P.[Rolf P.],
Similarity Rank Correlation for Face Recognition Under Unenrolled Pose,
ICB07(67-76).
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Ho, W.H.[Wai Han], Watters, P.[Paul], Verity, D.[Dominic],
Are Younger People More Difficult to Identify or Just a Peer-to-Peer Effect,
CAIP07(351-359).
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Hu, Y.X.[Yu-Xiao], Zhang, Z.Q.[Zhen-Qiu], Xu, X.[Xun], Fu, Y.[Yun], Huang, T.S.[Thomas S.],
Building Large Scale 3D Face Database for Face Analysis,
MCAM07(343-350).
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Ho, W.H.[Wai Han], Watters, P.[Paul],
A New Performance Evaluation Method for Face Identification: Regression Analysis of Misidentification Risk,
CVPR07(1-6).
IEEE DOI 0706
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Ho, W.H.[Wai Han], Watters, P.[Paul], Verity, D.[Dominic],
Robustness of the New Owner-Tester Approach for Face Identification Experiments,
Biometrics07(1-6).
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Zou, C.R.[Cai-Rong], Sun, N.[Ning], Ji, Z.H.[Zhen-Hai], Zhao, L.[Li],
2DCCA: A Novel Method for Small Sample Size Face Recognition,
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IEEE DOI 0702
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Sakaue, F.[Fumihiko], Kobayashi, M.[Makoto], Migita, T.[Tsuyoshi], Shakunaga, T.[Takeshi],
A Real-life Test of Face Recognition System for Dialogue Interface Robot in Ubiquitous Environments,
ICPR06(III: 1155-1160).
IEEE DOI 0609
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Wang, P.[Peng], Ji, Q.A.[Qi-Ang],
Performance Modeling and Prediction of Face Recognition Systems,
CVPR06(II: 1566-1573).
IEEE DOI 0606
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Aggarwal, G., Biswas, S., Chellappa, R.,
UMD Experiments with FRGC Data,
FRGC05(III: 172-172).
IEEE DOI 0507
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Maurer, T., Guigonis, D., Maslov, I., Pesenti, B., Tsaregorodtsev, A., West, D., Medioni, G.,
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FRGC05(III: 154-154).
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Hamsici, O.C., Martynez, A.M.,
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Xie, B.L.[Bing-Long], Ramesh, V.[Visvanathan], Zhu, Y.[Ying], Boult, T.E.[Terry E.],
On Channel Reliability Measure Training for Multi-Camera Face Recognition,
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Boult, T.E.,
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Ekenel, H.K.[Hazim Kemal], Szasz-Toth, L.[Lorant], Stiefelhagen, R.[Rainer],
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Stiefelhagen, R.[Rainer], Bernardin, K.[Keni], Ekenel, H.K.[Hazim K.], Voit, M.[Michael],
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Ekenel, H.K., Pnevmatikakis, A.,
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IEEE DOI 0604
CHIL: Computers in the Human Interaction Loop (
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Messer, K.[Kieron], Kittler, J.V.[Josef V.], Short, J.[James], Heusch, G., Cardinaux, F.[Fabien], Marcel, S.[Sebastien], Rodriguez, Y.[Yann], Shan, S.G.[Shi-Guang], Su, Y., Gao, W.[Wen], Chen, X.,
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Li, C.C.[Cong-Cong], Su, G.D.[Guang-Da], Meng, K.[Kai], Zhou, J.[Jun],
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Sumi, K.[Kazuhiko], Liu, C.[Chang], Matsuyama, T.[Takashi],
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ISVC05(207-218).
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Daum, H.[Henning],
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AFGR04(321-326).
IEEE DOI 0411
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Sherrah, J.,
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AFGR04(189-194).
IEEE DOI 0411
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Momeni, H.[Hajar], Sadeghi, M.T.[Mohammad T.], Abutalebi, H.R.[Hamid R.],
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ICPR04(II: 19-22).
IEEE DOI 0409
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Omachi, S., Sun, F.[Fang], Aso, H.,
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ICPR04(I: 220-223).
IEEE DOI 0409
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Popovici, V.[Vlad], Thiran, J.P.[Jean-Philippe], Rodriguez, Y., Marcel, S.,
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ICPR04(I: 313-317).
IEEE DOI 0409
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Li, Q.[Qi], Ye, J.P.[Jie-Ping], Kambhamettu, C.[Chandra],
Linear projection methods in face recognition under unconstrained illuminations: a comparative study,
CVPR04(II: 474-481).
IEEE DOI 0408
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Zhou, J.[Jie], Zhang, D.,
Face recognition by combining several algorithms,
ICPR02(III: 497-500).
IEEE DOI 0211
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Hwang, B.W.[Bon-Woo], Roh, M.C.[Myung-Cheol], Lee, S.W.[Seong-Whan],
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AFGR04(278-283).
IEEE DOI 0411
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Hwang, B.W.[Bon-Woo], Byun, H.R.[Hye-Ran], Roh, M.C.[Myung-Cheol], Lee, S.W.[Seong-Whan],
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Grother, P.J., Phillips, P.J.,
Models of large population recognition performance,
CVPR04(II: 68-75).
IEEE DOI 0408
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Micheals, R.J.[Ross J.], Grother, P.J.[Patrick J.], Phillips, P.J.[P. Jonathon],
The NIST HumanID Evaluation Framework,
AVBPA03(403-411).
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Thomas, D., Bowyer, K.W.[Kevin W.], Flynn, P.J.[Patrick J.],
Multi-frame Approaches To Improve Face Recognition,
Motion07(19-19).
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Flynn, P.J.[Patrick J.], Bowyer, K.W.[Kevin W.], Phillips, P.J.[P. Jonathon],
Assessment of Time Dependency in Face Recognition: An Initial Study,
AVBPA03(44-51).
Springer DOI 0310
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Gupta, H., Agrawal, A.K., Pruthi, T., Shekhar, C., Chellappa, R.,
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Phillips, P.J., Newton, E.M.,
Meta-analysis of Face Recognition Algorithms,
AFGR02(224-230).
IEEE DOI 0206
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And: EEMCV01(xx-yy). 0110

See also Meta-Analysis of Third-Party Evaluations of Iris Recognition. BibRef

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Hyperspectral Face Database,
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Dataset, Faces. BibRef

Liu, X.M.[Xiao-Ming], Chen, T.H.[Tsu-Han],
Geometry-assisted statistical modeling for face mosaicing,
ICIP03(II: 883-886).
IEEE DOI 0312
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Qidwai, S.[Saim], Venkataramani, K.[Krithika], Gutta, S.[Srinivas], Wechsler, H.[Harry],
Face Recognition Using Asymmetric Faces,
ICBA04(162-168).
Springer DOI 0505
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Gutta, S.[Srinivas], Wechsler, H.[Harry],
Partial Faces for Face Recognition: Left vs Right Half,
CAIP03(630-637).
Springer DOI 0311
BibRef

Gutta, S., Philomin, V., Trajkovic, M.,
An investigation into the use of partial-faces for face recognition,
AFGR02(28-33).
IEEE DOI 0206
BibRef

Yacoob, Y., Davis, L.S.,
Smiling faces are better for face recognition,
AFGR02(52-57).
IEEE DOI 0206
BibRef

Gutta, S.[Srinivas], Huang, J.[Jeffrey], Liu, C.J.[Cheng-Jun], Wechsler, H.[Harry],
Comparative Performance Evaluation of Gray-Scale and Color Information for Face Recognition Tasks,
AVBPA01(38).
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Bigun, J.[Josef], Choy, K.W.[Kwok-Wai], Olsson, H.[Henrik],
Evidence on Skill Differences of Women and Men Concerning Face Recognition,
AVBPA01(44).
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Kosmerlj, M.[Marijana], Fladsrud, T.[Tom], Hjelmĺs, E.[Erik], Snekkenes, E.[Einar],
Face Recognition Issues in a Border Control Environment,
ICB06(33-39).
Springer DOI 0601
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Hjelmĺs, E.[Erik], Farup, I.[Ivar],
A Comparison of Face/Non-face Classifiers,
AVBPA01(65).
Springer DOI 0310
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Wallhoff, F., Eickeler, S., Rigoll, G.,
A Comparison of Discrete and Continuous Output Modeling Techniques for a Pseudo-2d Hidden Markov Model Face Recognition System,
ICIP01(II: 685-688).
IEEE DOI 0108
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Eickeler, S.[Stefan], Jabs, M.[Mirco], Rigoll, G.[Gerhard],
Comparison of Confidence Measures for Face Recognition,
AFGR00(257-262).
IEEE DOI 0003
BibRef

Alvira, M.[Mariano], Rifkin, R.[Ryan],
An Empirical Comparison of SNoW and SVMs for Face Detection,
MIT AI Memo-2001-004, January 2001.
WWW Link. 0105
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Varsta, M., Heikkonen, J., Millán, J.D.R.[Jose Del R.], Mourińo, J.,
Evaluating the Performance of Three Feature Sets for Brain-computer Interfaces with an Early Stopping MLP Committee,
ICPR00(Vol II: 907-910).
IEEE DOI 0009
BibRef

Sukthankar, G.,
Face Recognition: A Critical Look at Biologically-Inspired Approaches,
CMU-RI-TR-00-04, January, 2000.
PDF File. BibRef 0001

Bruce, V.[Vicki], Burton, A.M.[A. Mike], Hancock, P.J.B.[Peter J.B.],
Comparisons between Human and Computer Recognition of Faces,
AFGR98(408-413).
IEEE DOI BibRef 9800

Li, S.Z., Lu, J.,
Generalizing Capacity of Face Database for Face Recognition,
AFGR98(402-406).
IEEE DOI BibRef 9800

Nakatsu, R.[Ryohei],
Nonverbal Information Recognition and Its Application to Communications,
AFGR98(2-7).
IEEE DOI BibRef 9800

Loui, A.C., Judice, C.N., Liu, S.[Sheng],
An image database for benchmarking of automatic face detection and recognition algorithms,
ICIP98(I: 146-150).
IEEE DOI 9810
BibRef

Ramsay, C.S., Sutherland, K., Renshaw, D., Denyer, P.B.,
A Comparison of Vector Quantization Codebook Generation Algorithms Applied to Automatic Face Recognition,
BMVC92(xx-yy).
PDF File. 9209
BibRef

Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Face Verification, Authentication, Evaluations, Verification Benchmarks .


Last update:Mar 16, 2024 at 20:36:19