Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

[1]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  F. Quimby What's in a picture? , 1993, Laboratory animal science.

[3]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[4]  David Beymer,et al.  Face recognition from one example view , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[6]  Jiri Matas,et al.  Statistical Chromaticity Models for Lip Tracking with B-splines , 1997, AVBPA.

[7]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[8]  D. B. Graham,et al.  Characterising Virtual Eigensignatures for General Purpose Face Recognition , 1998 .

[9]  A. Martínez,et al.  The AR face databasae , 1998 .

[10]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[11]  J. Cohn,et al.  Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding. , 1999, Psychophysiology.

[12]  Jiri Matas,et al.  XM2VTSDB: The Extended M2VTS Database , 1999 .

[13]  Vicki Bruce,et al.  Face Recognition: From Theory to Applications , 1999 .

[14]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Matti Pietikäinen,et al.  Physics-based face database for color research , 2000, J. Electronic Imaging.

[16]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[17]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  尚弘 島影 National Institute of Standards and Technologyにおける超伝導研究及び生活 , 2001 .

[19]  K. Kryszczuk,et al.  Color Correction for Face Detection Based on Human Visual Perception Metaphor , 2003 .

[20]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[22]  Richard B. Reilly,et al.  A colour face image database for benchmarking of automatic face detection algorithms , 2003, Proceedings EC-VIP-MC 2003. 4th EURASIP Conference focused on Video/Image Processing and Multimedia Communications (IEEE Cat. No.03EX667).

[23]  Heinrich H. Bülthoff,et al.  The MPI VideoLab: A system for high quality synchronous recording of video and audio from multiple viewpoints , 2004 .

[24]  Volker Blanz,et al.  Component-Based Face Recognition with 3D Morphable Models , 2004, CVPR Workshops.

[25]  Tamara L. Berg,et al.  Names and faces in the news , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[26]  Allen R. Hanson,et al.  Segmenting images using localized histograms and region merging , 1987, International Journal of Computer Vision.

[27]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[28]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[29]  Erik G. Learned-Miller,et al.  Learning Hyper-Features for Visual Identification , 2004, NIPS.

[30]  Erik G. Learned-Miller,et al.  Building a classification cascade for visual identification from one example , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[31]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  Greg Mori,et al.  Guiding model search using segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[33]  Pietro Perona,et al.  Pruning training sets for learning of object categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[34]  Alexei A. Efros,et al.  Recovering Surface Layout from an Image , 2007, International Journal of Computer Vision.

[35]  Pinar Duygulu Sahin,et al.  A Graph Based Approach for Naming Faces in News Photos , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[36]  Erik G. Learned-Miller,et al.  Discriminative Training of Hyper-feature Models for Object Identification , 2006, BMVC.

[37]  Frédéric Jurie,et al.  Learning Visual Similarity Measures for Comparing Never Seen Objects , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[38]  Erik G. Learned-Miller,et al.  Unsupervised Joint Alignment of Complex Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[39]  David A. Forsyth,et al.  Unsupervised Segmentation of Objects using Efficient Learning , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Wen Gao,et al.  The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[41]  Conrad Sanderson,et al.  Biometric Person Recognition: Face, Speech and Fusion , 2008 .