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The Application of Rough Set Theory and Near Set Theory to Face Recognition Problem
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Author(s): K R. Singh (Yeshwantrao Chavan College of Engineering, India), M M. Raghuwanshi (Yeshwantrao Chavan College of Engineering, India), M A. Zaveri (Sardar Vallabhbhai National Institute of Technology, India)and James F. Peters (University of Manitoba, Canada)
Copyright: 2016
Pages: 36
Source title:
Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications
Source Author(s)/Editor(s): Siddhartha Bhattacharyya (RCC Institute of Information Technology, India), Pinaki Banerjee (Goldstone Infratech Limited, India), Dipankar Majumdar (RCC Institute of Information Technology, India)and Paramartha Dutta (Visva-Bharati University, India)
DOI: 10.4018/978-1-4666-9474-3.ch013
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Abstract
Computer vision is a process of electronically perceiving and understanding of an image like human vision system (HVS) do. Face recognition techniques (FRT) determines the identity of the individual by matching the facial images with the one stored in the facial database. The performance of FRT is greatly affected by variations in face due to different factors. It is interesting to study how well these issues are being handled by RST and near set theory to improve the performance. The variation in illumination and plastic surgery changes the appearance of face that introduces imprecision and vagueness. One part of chapter introduces the adaptive illumination normalization technique using RST that classifies the image illumination into three classes based on which illumination normalization is performed using an appropriate filter. Later part of this chapter introduces use of near set theory for FRT on facial images that have previously undergone some feature modifications through plastic surgery.
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