PDSS: patch-descriptor-similarity space for effective face verification

In this paper, we propose the Patch-Descriptor-Similarity Space (PDSS) for unconstrained face verification, which is challenging due to image variations in pose, lighting, facial expression, and occlusion. Our proposed PDSS considers jointly patch, descriptor and similarity measure, which are ignored by the existing work. PDSS is extremely effective for face verification because each axis of PDSS will boost each other and could maximize the effect of every axis. Each point in PDSS reflects a distinct partial-matching between two facial images, which could be robust to variations in the facial images. Moreover, by selecting the discriminating point subset from PDSS, we could describe accurately the characteristic similarities and differences between two facial images, and further decide whether they represent the same person. In PDSS, each axis can describe effectively the distinct features of the faces: each patch (the first axis) reflects a distinct trait of a face; the descriptor (the second axis) is used to describe such face trait; and the similarity between two features can be measured by a certain kind of similarity measure (the third axis). The experiment adopts the extensively-used Labeled Face in the Wild (LFW) unconstrained face recognition dataset (13K faces), and our proposed PDSS approach achieves the best result, compared with the state-of-the-art methods.

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

[2]  Tal Hassner,et al.  Similarity Scores Based on Background Samples , 2009, ACCV.

[3]  Gang Hua,et al.  A robust elastic and partial matching metric for face recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

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

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

[8]  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).

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

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

[11]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

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

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

[14]  Cordelia Schmid,et al.  Automatic face naming with caption-based supervision , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Yan-Ying Chen,et al.  Semi-supervised face image retrieval using sparse coding with identity constraint , 2011, ACM Multimedia.

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

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

[18]  Nicolas Pinto,et al.  How far can you get with a modern face recognition test set using only simple features? , 2009, CVPR.