IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Active Appearance Models Revisited
International Journal of Computer Vision
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
FaceTracer: A Search Engine for Large Collections of Images with Faces
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Discriminant sparse nonnegative matrix factorization
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Are sparse representations really relevant for image classification?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Shiftable multiscale transforms
IEEE Transactions on Information Theory - Part 2
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Sparse representations, motivated by strong evidence of sparsity in the primate visual cortex, are gaining popularity in the computer vision and pattern recognition fields, yet sparse methods have not gained widespread acceptance in the facial understanding communities. A main criticism brought forward by recent publications is that sparse reconstruction models work well with controlled datasets, but exhibit coefficient contamination in natural datasets. To better handle facial understanding problems, specifically the broad category of facial classification problems, an improved sparse paradigm is introduced in this paper. Our paradigm combines manifold learning for dimensionality reduction, based on a newly introduced variant of semi-supervised Locality Preserving Projections, with a @?^1 reconstruction error, and a regional based statistical inference model. We demonstrate state-of-the-art classification accuracy for the facial understanding problems of expression, gender, race, glasses, and facial hair classification. Our method minimizes coefficient contamination and offers a unique advantage over other facial classification methods when dealing with occlusions. Experimental results are presented on multi-class as well as binary facial classification problems using the Labeled Faces in the Wild, Cohn-Kanade, Extended Cohn-Kanade, and GEMEP-FERA datasets demonstrating how and under what conditions sparse representations can further the field of facial understanding.