Manifold based sparse representation for facial understanding in natural images

摘要

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 ℓ1 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.