Facial Expression Recognition by Sparse Reconstruction with Robust Features

Facial expression analysis relies on the accurate detection of a few subtle face traces. According to specialists [3], facial expressions can be decomposed into a set of small Action Units (AU) corresponding to different face regions. In this paper, we propose to detect facial expressions with sparse reconstruction methods. Inspired by sparse regularization and sparse over-complete dictionaries, we aim at finding the minimal set of face atoms that can represent a given expression. l 1 based reconstruction computes the deviation from the average face as an additive model of facial expression atoms and classify unknown expressions accordingly. We compared the proposed approach to existing methods on the well-known Cohn-Kanade (CK+) dataset [6]. Results indicate that sparse reconstruction with l 1 penalty outperforms SVM and k-NN baselines with the tested features. The best accuracy (97%) was obtained using sparse reconstruction in an unsupervised setting.