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Deep cascade model-based face recognition: When deep-layered learning meets small data
Zhang,Lei1; Liu,Ji1; Zhang,Bob2; Zhang,David3; Zhu,Ce4
2019-09
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
Volume29Pages:1016-1029
Abstract

Sparse representation based classification (SRC), nuclear-norm matrix regression (NMR), and deep learning (DL) have achieved a great success in face recognition (FR). However, there still exist some intrinsic limitations among them. SRC and NMR based coding methods belong to one-step model, such that the latent discriminative information of the coding error vector cannot be fully exploited. DL, as a multi-step model, can learn powerful representation, but relies on large-scale data and computation resources for numerous parameters training with complicated back-propagation. Straightforward training of deep neural networks from scratch on small-scale data is almost infeasible. Therefore, in order to develop efficient algorithms that are specifically adapted for small-scale data, we propose to derive the deep models of SRC and NMR. Specifically, in this paper, we propose an end-to-end deep cascade model (DCM) based on SRC and NMR with hierarchical learning, nonlinear transformation and multi-layer structure for corrupted face recognition. The contributions include four aspects. First, an end-to-end deep cascade model for small-scale data without back-propagation is proposed. Second, a multi-level pyramid structure is integrated for local feature representation. Third, for introducing nonlinear transformation in layer-wise learning, softmax vector coding of the errors with class discrimination is proposed. Fourth, the existing representation methods can be easily integrated into our DCM framework. Experiments on a number of small-scale benchmark FR datasets demonstrate the superiority of the proposed model over state-of-the-art counterparts. Additionally, a perspective that deep-layered learning does not have to be convolutional neural network with back-propagation optimization is consolidated. The demo code is available in https://github.com/liuji93/DCM.

KeywordCorruption Deep Cascade Model Face Recognition Representation Learning Softmax Vector
DOI10.1109/TIP.2019.2938307
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligenceengineering, Electrical & Electronic
WOS IDWOS:000498872600003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141
Scopus ID2-s2.0-85072163241
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Citation statistics
Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang,Lei
Affiliation1.School of Microelectronics and Communication Engineering,Chongqing University,Chongqing,400044,China
2.Department of Computer and Information Science,University of Macau,Macao
3.School of Science and Engineering,Chinese University of Hong Kong at Shenzhen,Shenzhen,518172,China
4.School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu,611731,China
Recommended Citation
GB/T 7714
Zhang,Lei,Liu,Ji,Zhang,Bob,et al. Deep cascade model-based face recognition: When deep-layered learning meets small data[J]. IEEE Transactions on Image Processing,2019,29:1016-1029.
APA Zhang,Lei,Liu,Ji,Zhang,Bob,Zhang,David,&Zhu,Ce.(2019).Deep cascade model-based face recognition: When deep-layered learning meets small data.IEEE Transactions on Image Processing,29,1016-1029.
MLA Zhang,Lei,et al."Deep cascade model-based face recognition: When deep-layered learning meets small data".IEEE Transactions on Image Processing 29(2019):1016-1029.
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