Face Recognition Technique Using Symbolic Linear Discriminant Analysis Method

  • P. S. Hiremath
  • C. J. Prabhakar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Techniques that can introduce low dimensional feature representation with enhanced discriminatory power are important in face recognition systems. This paper presents one of the symbolic factor analysis method i.e., symbolic Linear Discriminant Analysis (symbolic LDA) method for face representation and recognition. Classical factor analysis methods extract features, which are single valued in nature to represent face images. These single valued variables may not be able to capture variation of each feature in all the images of same subject; this leads to loss of information. The symbolic Linear Discriminant Analysis Algorithm extracts most discriminating interval type features; they optimally discriminate among the classes represented in the training set. The proposed method has been successfully tested for face recognition using two databases, ORL and Yale Face database. The effectiveness of the proposed method is shown in terms of comparative performance against popular classical factor analysis methods such as eigenface method and Linear Discriminant Analysis method. Experimental results show that symbolic LDA outperforms the classical factor analysis methods.


Face Recognition Linear Discriminant Analysis Independent Component Analysis Face Image Kernel Principal Component Analysis 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • P. S. Hiremath
    • 1
  • C. J. Prabhakar
    • 2
  1. 1.Department of Studies in Computer ScienceGulbarga University, GulbargaKarnatakaIndia
  2. 2.Department of Studies in Computer ScienceKuvempu University, ShankaraghattaKarnatakaIndia

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