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NEURO FUZZY MODEL FOR FACE RECOGNITION WITH CURVELET BASED FEATURE IMAGE

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Author(s): SHREEJA R, | KHUSHALI DEULKAR, | SHALINI BHATIA

Journal: International Journal of Engineering Science and Technology
ISSN 0975-5462

Volume: 3;
Issue: 6;
Start page: 5306;
Date: 2011;
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Keywords: Curvelet transform | Neuro fuzzy | Statistical quantities

ABSTRACT
A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometric techniques such as fingerprint or iris recognition systems. Every face has approximately 80 nodal points like (Distance between the eyes, Width of the nose etc).The basic face recognition system capture the sample, extract feature, compare template and perform matching. In this paper two methods of face recognition are compared- neural networks and neuro fuzzy method. For this curvelet transform is used for feature extraction. Feature vector is formed by extracting statistical quantities of curve coefficients. From the statistical results it is concluded that neuro fuzzy method is the better technique for face recognition as compared to neural network.
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