Face recognition using feature coding and nonlocal constraint-based sparse representation classifier

Mengmeng Liao; Xiaodong Gu

doi:10.1117/1.JEI.27.5.053050

31 October 2018 Face recognition using feature coding and nonlocal constraint-based sparse representation classifier

Mengmeng Liao, Xiaodong Gu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053050 (October 2018). https://doi.org/10.1117/1.JEI.27.5.053050

Abstract

We propose a face recognition method using distinctive triangle encoding and nonlocal constraint-based sparse representation (TENCSR). With TENCSR, first the pixel values of all images are used as the low-level features. Next, a clustering method is proposed by considering the density distribution of target data, named shared weights support vector data description (SW-SVDD), which makes the obtained decision boundary closer to the optimal. On the basis of SW-SVDD, a more distinctive triangle encoding (MDTE) method is introduced by considering the cluster center information and the size information of cluster, which makes the encoded features more distinctive. Then the high-level features are obtained by encoding those low-level features using MDTE. Meanwhile, a nonlocal constraint-based sparse representation classifier (NC-SRC) is proposed by the biological discovery that dissimilar inputs have dissimilar codes. Finally, those high-level features are classified by the proposed NC-SRC. Experimental results on Georgia Tech, CVL, IMM, FRGC, and AR databases show that our TENCSR outperforms some state-of-the-art algorithms.

Citation Download Citation

Mengmeng Liao and Xiaodong Gu "Face recognition using feature coding and nonlocal constraint-based sparse representation classifier," Journal of Electronic Imaging 27(5), 053050 (31 October 2018). https://doi.org/10.1117/1.JEI.27.5.053050

Received: 4 June 2018; Accepted: 2 October 2018; Published: 31 October 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available