Face recognition method based on SVM and SRC
Chongyang Gao, Hongjuan Zhu, Lihua Liao · 2019
For face recognition with global features, Support Vector Machines (SVM) and Sparse Representation Classification (SRC) are two methods which are difficult to take into account the recognition effect and efficiency when used alone. Based on the study of the two methods, this paper proposes a global linear face recognition method which combines Support Vector Machines and Sparse Representation Classification. On account of the recognition error of SVM, Sparse representation model is solved by Augmented Lagrange Multiplier method and the second recognition is carried out. In addition, the results on YALE show that this method can significantly improve the accuracy and speed of recognition.