patch based collaborative representation with gabor feature and measurement matrix for face recognition

Clicks: 75
ID: 221643
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #842 of 1,093 articles by views in journal of power sources

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,093 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In recent years, sparse representation based classification (SRC) has emerged as a popular technique in face recognition. Traditional SRC focuses on the role of the l1-norm but ignores the impact of collaborative representation (CR), which employs all the training examples over all the classes to represent a test sample. Due to issues like expression, illumination, pose, and small sample size, face recognition still remains as a challenging problem. In this paper, we proposed a patch based collaborative representation method for face recognition via Gabor feature and measurement matrix. Using patch based collaborative representation, this method can solve the problem of the lack of accuracy for the linear representation of the small sample size. Compared with holistic features, the multiscale and multidirection Gabor feature shows more robustness. The usage of measurement matrix can reduce large data volume caused by Gabor feature. The experimental results on several popular face databases including Extended Yale B, CMU_PIE, and LFW indicated that the proposed method is more competitive in robustness and accuracy than conventional SR and CR based methods.
Reference Key
xu2018mathematicalpatch Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhengyuan Xu;Yu Liu;Mingquan Ye;Lei Huang;Hao Yu;Xun Chen
Journal journal of power sources
Year 2018
DOI
10.1155/2018/3025264
URL
http://dx.doi.org/10.1155/2018/3025264
Keywords
civil engineering (general)mathematics engineering (general)

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.