Abstract
In This Paper, We Propose a Novel Method for Fast Face Recognition Called L1/2-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 L1/2 Regularization, Our Approach Can Produce Sparser and More Robust Representation Compared to L1-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.
Recommended Citation
B. Han et al., "HSR: L 1/2-Regularized Sparse Representation for Fast Face Recognition using Hierarchical Feature Selection," Neural Computing and Applications, vol. 27, no. 2, pp. 305 - 320, Springer, Feb 2016.
The definitive version is available at https://doi.org/10.1007/s00521-015-1907-y
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
ELM-AE; Fast face recognition; Gabor wavelets; Hierarchical feature selection; HSR; L regularization 1/2; Sparse representation
International Standard Serial Number (ISSN)
0941-0643
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Feb 2016
Comments
National Natural Science Foundation of China, Grant 31202036