title: A Modified Sparse Representation Method for Facial Expression Recognition.
abstract: In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.
authors.affiliation: Department of Control Science and Engineering, School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of Control Science and Engineering, School of Electronics and Information Engineering, Tongji University, Shanghai 200092, China.
authors.forename: LiHong
authors.initials: L
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D005149:Facial Expression; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D010364:Pattern Recognition, Visual / Q000502:physiology*