Affiliations: [a] Department of Artificial Intelligence, Chongqing Creation Vocational College, Chongqing, China | [b] Department of Economics and Management, Chongqing Creation Vocational College, Chongqing, China | [c] Education and Information Technology Center, China West Normal University, Nanchong, Sichuan, China
Correspondence:
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Corresponding author: Chun He, Education and Information Technology Center, China West Normal University, Nanchong, Sichuan, China. E-mail: [email protected].
Abstract: At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions.
To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed
in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer
by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test
samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition
rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and
universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well,
which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face
recognition.
Keywords: Face recognition, deep learning, LBP, unconstrained conditions
