[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3745/JIPS.01.0011

Homogeneous and Non-homogeneous Polynomial Based Eigenspaces to Extract the Features on Facial Images

Muntasa, Arif (Dept. of Informatics Engineering, University of Trunojoyo)

Publication Information

Journal of Information Processing Systems / v.12, no.4, 2016 , pp. 591-611 More about this Journal

Abstract

High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces.

Keywords

Eigenspaces; Feature Extraction; Homogeneous; Non-homogeneous;

Citations & Related Records

Reference

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