Open AccessFace Recognition Based on Deep Features
Author(s) -
Yi Li,
Sun Zhen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012157
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , robustness (evolution) , convolutional neural network , face (sociological concept) , feature (linguistics) , facial recognition system , feature extraction , deep learning , feature vector , computer vision , social science , biochemistry , chemistry , linguistics , philosophy , sociology , gene
A face recognition method based on deep feature decision fusion is proposed. First, a convolutional neural network (CNN) is designed for deep feature learning on face images. Then, all the feature are mapped to construct a single depth feature vector. In the classification stage, the sparse representation-based classification is used to characterize the constructed depth feature vector, and the face category of the object to be recognized is determined according to the overall reconstruction error. Experiments are carried out on ORL and Yale-B datasets and compared with several existing face recognition methods. The results verify the effectiveness and robustness of the proposed method.