Face Recognition Using Composite Features Based on Discriminant Analysis

Sang Il Choi, Sung Sin Lee, Sang Tae Choi, Won Yong Shin

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Extracting holistic features from the whole face and extracting the local features from the sub-image have pros and cons depending on the conditions. In order to effectively utilize the strengths of various types of holistic features and local features while also complementing each weakness, we propose a method to construct a composite feature vector for face recognition based on discriminant analysis. We first extract the holistic features and the local features from the whole face image and various types of local images using the discriminant feature extraction method. Then, we measure the amount of discriminative information in the individual holistic features and local features and construct composite features with only discriminative features for face recognition. The composite features from the proposed method were compared with the holistic features, local features, and others prepared by hybrid methods through face recognition experiments for various types of face image databases. The proposed composite feature vector displayed better performance than the other methods.

Original languageEnglish
Pages (from-to)13663-13670
Number of pages8
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 2018 Mar 5

Bibliographical note

Funding Information:
This work was supported in part by the Research Fund of Dankook University in 2016, in part by the National Research Foundation of Korea Grant through the Korean Government (MSIT) under Grant 2018R1A2B6001400, and in part by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning from the Ministry of Trade, Industry and Energy, South Korea, under Grant 20174030201740.

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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