NDLI: HSR: L 1/2-regularized sparse representation for fast face recognition using hierarchical feature selection

Content Provider	Springer Nature Link
Author	Han, Bo He, Bo Sun, Tingting Yan, Tianhong Ma, Mengmeng Shen, Yue Lendasse, Amaury
Copyright Year	2015
Abstract	In this paper, we propose a novel method for fast face recognition called L 1/2-regularized sparse representation using hierarchical feature selection. By employing hierarchical feature selection, we can compress the scale and dimension of global dictionary, which directly contributes to the decrease of computational cost in sparse representation that our approach is strongly rooted in. It consists of Gabor wavelets and extreme learning machine auto-encoder (ELM-AE) hierarchically. For Gabor wavelets’ part, local features can be extracted at multiple scales and orientations to form Gabor-feature-based image, which in turn improves the recognition rate. Besides, in the presence of occluded face image, the scale of Gabor-feature-based global dictionary can be compressed accordingly because redundancies exist in Gabor-feature-based occlusion dictionary. For ELM-AE part, the dimension of Gabor-feature-based global dictionary can be compressed because high-dimensional face images can be rapidly represented by low-dimensional feature. By introducing L 1/2 regularization, our approach can produce sparser and more robust representation compared to L 1-regularized sparse representation-based classification (SRC), which also contributes to the decrease of the computational cost in sparse representation. In comparison with related work such as SRC and Gabor-feature-based SRC, experimental results on a variety of face databases demonstrate the great advantage of our method for computational cost. Moreover, we also achieve approximate or even better recognition rate.
Starting Page	305
Ending Page	320
Page Count	16
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-04-17
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fast face recognition Hierarchical feature selection Gabor wavelets ELM-AE Sparse representation L 1/2 regularization HSR Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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