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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.
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篇名 Hierarchical Representations Feature Deep Learning for Face Recognition
来源期刊 数据分析和信息处理(英文) 学科 工学
关键词 Face Recognition UNSUPERVISED Hierarchical Representations Hybrid Neural Networks RBM Deep Belief Network Deep Learning
年,卷(期) 2020,(3) 所属期刊栏目
研究方向 页码范围 195-227
页数 33页 分类号 TP1
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节点文献
Face
Recognition
UNSUPERVISED
Hierarchical
Representations
Hybrid
Neural
Networks
RBM
Deep
Belief
Network
Deep
Learning
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引文网络交叉学科
相关学者/机构
期刊影响力
数据分析和信息处理(英文)
季刊
2327-7211
武汉市江夏区汤逊湖北路38号光谷总部空间
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106
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0
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