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Comparative Study of Feature Extraction Techniques for Face Recognition System

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IJCA Proceedings on National Conference on Recent Trends in Computing
© 2012 by IJCA Journal
NCRTC - Number 6
Year of Publication: 2012
Authors:
J. K. Keche
M. P. Dhore

J K Keche and M P Dhore. Article: Comparative Study of Feature Extraction Techniques for Face Recognition System. IJCA Proceedings on National Conference on Recent Trends in Computing NCRTC(6):1-5, May 2012. Full text available. BibTeX

@article{key:article,
	author = {J. K. Keche and M. P. Dhore},
	title = {Article: Comparative Study of Feature Extraction Techniques for Face Recognition System},
	journal = {IJCA Proceedings on National Conference on Recent Trends in Computing},
	year = {2012},
	volume = {NCRTC},
	number = {6},
	pages = {1-5},
	month = {May},
	note = {Full text available}
}

Abstract

Face recognition is one of the most active research areas in computer vision and pattern recognition. This paper compares the different face recognition techniques like visual face recognition, thermal face recognition, eigenface approach and feature extraction techniques like geometry-based feature extraction(Gabor wavelet transform), appearance based techniques, color segmentation based techniques and template based feature extraction. PCA is used in extracting the relevant information in human face. Face images are projected on to the face space which encodes the variation among known face images. This paper discusses feature extraction techniques with pros and cons. Performances of these techniques are different with various factors such as face expression variation, illumination variation, noise and orientation. Visual face recognition systems perform relatively reliably under controlled illumination conditions. Thermal face recognition systems are advantageous for detecting disguised faces or when there is no control over illumination. Thermal images of individuals wearing eyeglasses may be poor performance since eyeglasses block the infrared emissions around the eyes, which are important features for recognition.

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