On a Face Recognition by the Modified Nonsingular Discriminant Analysis for a Ubiquitous Computing

  • Jin Ok Kim
  • Kwang Hoon Jung
  • Chin Hyun Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


This paper presents an efficient face recognition by the modified nonsingular discriminant analysis for a ubiquitous computing. It is popular to extract discriminant features using Fisher linear discriminant analysis (LDA) for general face recognition. In this paper, we propose the modified nonsingular discriminant analysis in order to overcome the problem of small sample size and prone to be unrealizable due to the singularity of scatter matrices. The scatter matrix of transformed features is nonsingular. From the experiments on facial databases, we find that the modified nonsingular discriminant feature extraction achieves significant face recognition performance compared to other LDA-related methods for a limited range of sample sizes and class numbers. Also, recognition by the modified nonsingular discriminant analysis by using TMS320C6711 DSP Vision Board is set to highlight the advantages of our algorithm.


Face Recognition Linear Discriminant Analysis Ubiquitous Computing Scatter Matrix Fisher Criterion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jin Ok Kim
    • 1
  • Kwang Hoon Jung
    • 2
  • Chin Hyun Chung
    • 2
  1. 1.Faculty of MultimediaDaegu Haany UniversityGyeongsangbuk-doKorea
  2. 2.Department of Information and Control EngineeringKwangwoon UniversitySeoulKorea

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