Fast High Dimensional Vector Multiplication Face Recognition

This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework. First, the Over-Complete Local Binary Patterns (OCLBP) face representation scheme is introduced as a multi-scale modified version of the Local Binary Patterns (LBP) scheme. Second, we propose an efficient matrix-vector multiplication-based recognition system. The system is based on Linear Discriminant Analysis (LDA) coupled with Within Class Covariance Normalization (WCCN). This is further extended to the unsupervised case by proposing an unsupervised variant of WCCN. Lastly, we introduce Diffusion Maps (DM) for non-linear dimensionality reduction as an alternative to the Whitened Principal Component Analysis (WPCA) method which is often used in face recognition. We evaluate the proposed framework on the LFW face recognition dataset under the restricted, unrestricted and unsupervised protocols. In all three cases we achieve very competitive results.

[1]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[2]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[3]  Jitendra Malik,et al.  Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[5]  C. Schmid,et al.  Description of Interest Regions with Center-Symmetric Local Binary Patterns , 2006, ICVGIP.

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

[7]  Andreas Stolcke,et al.  Generalized Linear Kernels for One-Versus-All Classification: Application to Speaker Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

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

[9]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

[10]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[11]  Yaniv Taigman,et al.  Descriptor Based Methods in the Wild , 2008 .

[12]  Tal Hassner,et al.  Multiple One-Shots for Utilizing Class Label Information , 2009, BMVC.

[13]  Cordelia Schmid,et al.  Is that you? Metric learning approaches for face identification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Nicolas Pinto,et al.  How far can you get with a modern face recognition test set using only simple features? , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Li Bai,et al.  Cosine Similarity Metric Learning for Face Verification , 2010, ACCV.

[17]  Jian Sun,et al.  Face recognition with learning-based descriptor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  William M. Campbell,et al.  Graph-embedding for speaker recognition , 2010, INTERSPEECH.

[19]  Nicolas Pinto,et al.  Beyond simple features: A large-scale feature search approach to unconstrained face recognition , 2011, Face and Gesture 2011.

[20]  Stéphane Mallat,et al.  Group Invariant Scattering , 2011, ArXiv.

[21]  Jian Sun,et al.  An associate-predict model for face recognition , 2011, CVPR 2011.

[22]  Lior Wolf,et al.  Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition , 2011, ArXiv.

[23]  Yang Zhao,et al.  An Efficient Multi-scale Overlapped Block LBP Approach for Leaf Image Recognition , 2012, ICIC.

[24]  Shenghuo Zhu,et al.  Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval , 2012, ArXiv.

[25]  Stéphane Mallat,et al.  Combined scattering for rotation invariant texture analysis , 2012, ESANN.

[26]  Peter N. Belhumeur,et al.  Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification , 2012, BMVC.

[27]  Umar Mohammed,et al.  Probabilistic Models for Inference about Identity , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Jian Sun,et al.  Bayesian Face Revisited: A Joint Formulation , 2012, ECCV.

[29]  Frédéric Jurie,et al.  Face Recognition using Local Quantized Patterns , 2012, BMVC.

[30]  Jian Sun,et al.  Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Stéphane Mallat,et al.  Invariant Scattering Convolution Networks , 2012, IEEE transactions on pattern analysis and machine intelligence.