Vision-Based Human Face Recognition Using Extended Principal Component Analysis

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

A. F. M. Saifuddin Saif, Anton Satria Prabuwono, Zainal Rasyid Mahayuddin, Teddy Mantoro
Copyright: © 2013 |Pages: 13
DOI: 10.4018/ijmcmc.2013100105
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Abstract

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.
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The human face is used to recognize individuals and the advancement in computing capability over the past few decades now enable similar recognition automatically (Raajan et al., 2012). Early face recognition algorithms use simple geometric models (Medeiros, Carrijo, Flores, & Veiga, 2012), but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes (Raajan et al., 2012; Dornaika & Bosaghzadeh, 2013). Previously, some researchers devoted to improve the performance of the PCA-based face recognition algorithms in various ways. Researchers in (Jian, Zhang, Frangi, & Jing-Yu, 2004; Kwang In, Keechul, & Hang Joon, 2002) extended PCA into two dimensional and kernel space, respectively. Researcher in (Perlibakas, 2004) analysed 14 distance measures for PCA based face recognition. However, these proposals could not solve the problems caused by a large number of classes and complicated illumination environment.

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