Set-based label propagation of face images

Graph-based Semi-Supervised Learning (SSL) has proven to be an effective tool for label propagation, however, its accuracy is highly dependent on how to form the data weight matrix, in which each element is obtained as the similarity between every pair of data points. Inspired by the success of set-based recognition methods, a novel approach is brought up to incorporate the set-to-set matching as well as single-to-single matching when building up the weight matrix. Canonical Correlation Analysis (CCA), which measures the principal angles between two manifolds, is adopted to compute the set similarity. Moreover, Local Binary Pattern, an effective texture descriptor, is investigated as a data representation to further improve the label propagation performance. The proposed approach is evaluated on two public face image data sets, and shown to significantly outperform the standard SSL methods in terms of accuracy.

[1]  Josef Kittler,et al.  Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[3]  Shin'ichi Satoh,et al.  Comparative evaluation of face sequence matching for content-based video access , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[4]  Ken-ichi Maeda,et al.  Face recognition using temporal image sequence , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[5]  Hong Cheng,et al.  Sparsity induced similarity measure for label propagation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  Xinhua Zhang,et al.  Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms , 2006, NIPS.

[7]  Trevor Darrell,et al.  Face Recognition from Long-Term Observations , 2002, ECCV.

[8]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[9]  Lior Wolf,et al.  Learning over Sets using Kernel Principal Angles , 2003, J. Mach. Learn. Res..

[10]  Zoubin Ghahramani,et al.  Learning from labeled and unlabeled data with label propagation , 2002 .

[11]  Gang Hua,et al.  Which faces to tag: Adding prior constraints into active learning , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Chi-Ho Chan.  Multi-scale local Binary Pattern Histogram for Face Recognition , 2007, ICB.