Logistic similarity metric learning for face verification

This paper presents a new method for similarity metric learning, called Logistic Similarity Metric Learning (LSML), where the cost is formulated as the logistic loss function, which gives a probability estimation of a pair of faces being similar. Especially, we propose to shift the similarity decision boundary gaining significant performance improvement. We test the proposed method on the face verification problem using four single face descriptors: LBP, OCLBP, SIFT and Gabor wavelets. Extensive experimental results on the LFW-a data set demonstrate that the proposed method achieves competitive state-of-the-art performance on the problem of face verification.

[1]  Marc Sebban,et al.  A Survey on Metric Learning for Feature Vectors and Structured Data , 2013, ArXiv.

[2]  Atilla Baskurt,et al.  Triangular similarity metric learning for face verification , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[3]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[5]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

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

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

[8]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[9]  Samy Bengio,et al.  Large Scale Online Learning of Image Similarity Through Ranking , 2009, J. Mach. Learn. Res..

[10]  Samy Bengio,et al.  Large Scale Online Learning of Image Similarity through Ranking , 2009, IbPRIA.

[11]  Xiaogang Wang,et al.  Hybrid Deep Learning for Face Verification , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Oren Barkan,et al.  Fast High Dimensional Vector Multiplication Face Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[13]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[14]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[15]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[16]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .

[17]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[19]  Joo-Hwee Lim,et al.  Similarity Learning for Nearest Neighbor Classification , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[20]  Stephen Tyree,et al.  Non-linear Metric Learning , 2012, NIPS.

[21]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[22]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[23]  Alice J. O'Toole,et al.  Fusing Face-Verification Algorithms and Humans , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Peng Li,et al.  Similarity Metric Learning for Face Recognition , 2013, 2013 IEEE International Conference on Computer Vision.

[25]  Fu Jie Huang,et al.  A Tutorial on Energy-Based Learning , 2006 .

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

[27]  Jiwen Lu,et al.  Discriminative Deep Metric Learning for Face Verification in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.