FER based on the improved convex nonnegative matrix factorization feature

Abstract

Facial expression recognition is an important research issue in the pattern recognition field. In this paper, we intend to present an accurate facial expression recognition (FER) system, which employs an improved convex non-negative matrix factorization (ICNMF) method based on a novel objective function and smaller iterative step sizes for feature extraction. Since negative values appearing in the facial expression feature will weaken the features and reduce the recognition rate, the nonnegative matrix factorization (NMF) methods are adopted to guarantee the non-negativity of the extracted feature value to improve the recognition rate. To enhance the performance of NMF method for FER, the ICNMF approach based on a novel convergent objective function and smaller iterative step sizes is proposed, and the FER rate can be improved effectively. In the FER system, the face region is detected firstly, and is enhanced by histogram specification, secondly the ICNMF approach is adopted to extract features and then the feature coefficient matrix is achieved. Finally, the SVM classifier is applied to recognize the extracted features. To validate the effectiveness of FER system, four public available datasets of MultiPIE, CK+, FER2013 and SFEW are tested and then high recognition rates can be achieved based on ICNMF method. In addition, the proposed ICNMF approach is compared with the methods of multi-layer NMF, sparse non-negative matrix factorization (SNMF), the traditional convex non-negative matrix factorization (CNMF), deep belief networks (DBN) and stacked auto-encoder (SAE), and results of experiments show that the proposed ICNMF approach is significantly effective contrasting to the other five expression extraction methods.