Boosted Metric Learning for Efficient Identity-Based Face Retrieval

This paper presents MLBoost, an efficient method for learning to compare face signatures , and shows its application to the hierarchical organization of large face databases. More precisely, the proposed metric learning (ML) algorithm is based on boosting so that the metric is learned iteratively by combining several weak metrics. Boosting allows our method to be free of any hyper-parameters (no cross-validation required) and to be robust with respect to overfitting. This MLBoost algorithm can be trained from constraints involving two pairs of vectors (quadruplets) with a quadratic complexity. The paper also shows how it can be included in a semi-supervised hierarchical clustering framework adapted to identity based face search. Our approach is validated on a benchmark relying on the Labelled Faces in the Wild (LFW) dataset supplemented with 1M face distractors.

[1]  Fei Xiong,et al.  Person Re-Identification Using Kernel-Based Metric Learning Methods , 2014, ECCV.

[2]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[3]  Vinod Nair,et al.  Learning hierarchical similarity metrics , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Lei Wang,et al.  Positive Semidefinite Metric Learning Using Boosting-like Algorithms , 2011, J. Mach. Learn. Res..

[5]  Jinbo Bi,et al.  AdaBoost on low-rank PSD matrices for metric learning , 2011, CVPR 2011.

[6]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

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

[8]  Andrew Zisserman,et al.  Fisher Vector Faces in the Wild , 2013, BMVC.

[9]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[10]  Yunhong Wang,et al.  Enhancing Person Re-identification by Robust Structural Metric Learning , 2013, 2013 Seventh International Conference on Image and Graphics.

[11]  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.

[12]  Horst Bischof,et al.  Large scale metric learning from equivalence constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  David R. Karger,et al.  Less is More Probabilistic Models for Retrieving Fewer Relevant Documents , 2006 .

[14]  Frédéric Jurie,et al.  PCCA: A new approach for distance learning from sparse pairwise constraints , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[16]  Alex M. Andrew,et al.  Boosting: Foundations and Algorithms , 2012 .

[17]  Jitendra Malik,et al.  Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[19]  Xuelong Li,et al.  Person Re-Identification by Regularized Smoothing KISS Metric Learning , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[23]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

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

[25]  Cordelia Schmid,et al.  Face recognition from caption-based supervision , 2010 .

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

[27]  Thomas Mensink,et al.  Image Classification with the Fisher Vector: Theory and Practice , 2013, International Journal of Computer Vision.

[28]  V. Vapnik.  Estimation of Dependences Based on Empirical Data , 2006 .

[29]  Jiwen Lu,et al.  Regularized Bayesian Metric Learning for Person Re-identification , 2014, ECCV Workshops.

[30]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[31]  Honglak Lee,et al.  Learning to Align from Scratch , 2012, NIPS.

[32]  Patrick Pérez,et al.  Some Faces are More Equal than Others: Hierarchical Organization for Accurate and Efficient Large-Scale Identity-Based Face Retrieval , 2014, ECCV Workshops.