Facial Trait Code

We propose a facial trait code (FTC) to encode human facial images, and apply it to face recognition. Extracted from an exhaustive set of local patches cropped from a large stack of faces, the facial traits and the associated trait patterns can accurately capture the appearance of a given face. The extraction has two phases. The first phase is composed of clustering and boosting upon a training set of faces with neutral expression, even illumination, and frontal pose. The second phase focuses on the extraction of the facial trait patterns from the set of faces with variations in expression, illumination, and poses. To apply the FTC to face recognition, two types of codewords, hard and probabilistic, with different metrics for characterizing the facial trait patterns are proposed. The hard codeword offers a concise representation of a face, while the probabilistic codeword enables matching with better accuracy. Our experiments compare the proposed FTC to other algorithms on several public datasets, all showing promising results.

[1]  Jörgen Ahlberg,et al.  Facial Feature Extraction using Deformable Graphs and Statistical Pattern Matching , 1999 .

[2]  Yi-Ping Hung,et al.  Face verification and identification using Facial Trait Code , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[4]  Stan Z. Li,et al.  Face recognition based on multiple facial features , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[5]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[6]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[7]  Wen Gao,et al.  Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition , 2007, IEEE Transactions on Image Processing.

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

[9]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Terry Windeatt,et al.  Boosted ECOC ensembles for face recognition , 2003 .

[11]  Yi-Ping Hung,et al.  Robust Face Recognition Using Probabilistic Facial Trait Code , 2010, ECCV.

[12]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 large-scale results , 2007 .

[13]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[14]  Thomas Serre,et al.  Hierarchical classification and feature reduction for fast face detection with support vector machines , 2003, Pattern Recognit..

[15]  Shuyan Zhao,et al.  Multiblock-fusion scheme for face recognition , 2004, ICPR 2004.

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

[17]  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).

[18]  B. V. K. Vijaya Kumar,et al.  Face class code based feature extraction for face recognition , 2005, Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05).

[19]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  Jiri Matas,et al.  Face verification using error correcting output codes , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Yi-Ping Hung,et al.  Subject-Specific and Pose-Oriented Facial Features for Face Recognition Across Poses , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[24]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[25]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Shimon Ullman,et al.  View-Invariant Recognition Using Corresponding Object Fragments , 2004, ECCV.

[27]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[28]  Ognjen Arandjelovic.  Unfolding a Face: From Singular to Manifold , 2009, ACCV.

[29]  Yi-Ping Hung,et al.  Face synthesis using Facial Trait Code and its application to creating suspect's physical profiles , 2009, 2009 IEEE International Conference on Multimedia and Expo.

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

[31]  Thomas Serre,et al.  Using component features for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[32]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[33]  Paola Campadelli,et al.  A face recognition system based on automatically determined facial fiducial points , 2006, Pattern Recognit..

[34]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[35]  Yi-Ping Hung,et al.  Cascading Multimodal Verification using Face, Voice and Iris Information , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[36]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[37]  Thomas Serre,et al.  A Component-based Framework for Face Detection and Identification , 2007, International Journal of Computer Vision.

[38]  Jian-Huang Lai,et al.  Component-based LDA method for face recognition with one training sample , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[39]  Zhi-Hua Zhou,et al.  Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble , 2005, IEEE Transactions on Neural Networks.

[40]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[41]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.