Orthogonal enhanced linear discriminant analysis for face recognition

Chuang Lin; Binghui Wang; Xin Fan; Yanchun Ma; Huiyun Liu

Orthogonal enhanced linear discriminant analysis for face recognition

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Author(s): Chuang Lin ^{1, 2} ; Binghui Wang ² ; Xin Fan ² ; Yanchun Ma ³ ; Huiyun Liu ⁴
- Affiliations: 1: Institute for Neurorehabilitation Systems, University Medical Center Göttingen, Georg-August University, Göttingen 37075, Germany ;
  2: School of Software, Dalian University of Technology, Developing Zone, Dalian City 116620, People's Republic of China ;
  3: Heilongjiang University of Traditional Chinese Medicine, 24 Heping Road, Harbin 150040, People's Republic of China ;
  4: Glasgow International College, Glasgow W14 0EE, UK
Source: Volume 5, Issue 2, June 2016, p. 100 – 110
DOI: 10.1049/iet-bmt.2014.0086 , Print ISSN 2047-4938, Online ISSN 2047-4946

Received 13/10/2014, Accepted 11/08/2015, Revised 06/07/2015, Published 01/06/2016

From the intuition that natural face images lie on or near a low-dimensional submanifold, the authors propose a novel spectral graph based dimensionality reduction method, named orthogonal enhanced linear discriminant analysis (OELDA), for face recognition. OELDA is based on enhanced LDA (ELDA), which takes into account both the discriminative structure and geometrical structure of the face space, and generates non-orthogonal basis vectors. However, a significant fact is that eliminating the dependence of basis vectors can promote more effective recognition of unseen face images. For this purpose, the authors seek to improve the ELDA scheme by imposing orthogonal constraints on the basis vectors. Experimental results on real-world face datasets show that, benefitting from orthogonality, OELDA has more locality preserving power and discriminative power than LDA and ELDA, and achieves the highest recognition rates among compared methods.

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