文章基本信息

标题：Hierarchical Representations Feature Deep Learning for Face Recognition
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作者：Haijun Zhang ; Yinghui Chen
期刊名称：Journal of Data Analysis and Information Processing
印刷版ISSN：2327-7211
电子版ISSN：2327-7203
出版年度：2020
卷号：8
期号：3
页码：195-227
DOI：10.4236/jdaip.2020.83012
出版社：Scientific Research Publishing
摘要：Most modern face recognition and classification systems mainly rely on hand-crafted image feature descriptors. In this paper, we propose a novel deep learning algorithm combining unsupervised and supervised learning named deep belief network embedded with Softmax regress (DBNESR) as a natural source for obtaining additional, complementary hierarchical representations, which helps to relieve us from the complicated hand-crafted feature-design step. DBNESR first learns hierarchical representations of feature by greedy layer-wise unsupervised learning in a feed-forward (bottom-up) and back-forward (top-down) manner and then makes more efficient recognition with Softmax regress by supervised learning. As a comparison with the algorithms only based on supervised learning, we again propose and design many kinds of classifiers: BP, HBPNNs, RBF, HRBFNNs, SVM and multiple classification decision fusion classifier (MCDFC)—hybrid HBPNNs-HRBFNNs-SVM classifier. The conducted experiments validate: Firstly, the proposed DBNESR is optimal for face recognition with the highest and most stable recognition rates; second, the algorithm combining unsupervised and supervised learning has better effect than all supervised learning algorithms; third, hybrid neural networks have better effect than single model neural network; fourth, the average recognition rate and variance of these algorithms in order of the largest to the smallest are respectively shown as DBNESR, MCDFC, SVM, HRBFNNs, RBF, HBPNNs, BP and BP, RBF, HBPNNs, HRBFNNs, SVM, MCDFC, DBNESR; at last, it reflects hierarchical representations of feature by DBNESR in terms of its capability of modeling hard artificial intelligent tasks.
关键词：Face Recognition;Unsupervised;Hierarchical Representations;Hybrid Neural Networks;RBM;Deep Belief Network;Deep Learning