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Analysis of Face Space for Recognition using Interval-Valued Subspace Technique
Abstract
The major contribution of the research work presented in this chapter is the development of effective face recognition algorithm using analysis of face space in the interval-valued subspace. The analysis of face images is used for various purposes such as facial expression classification, gender determination, age estimation, emotion assessment, face recognition, et cetera. The research community of face image analysis has developed many techniques for face recognition; one of the successful techniques is based on subspace analysis. In the first part of the chapter, the authors present discussion of earliest face recognition techniques, which are considered as mile stones in the roadmap of subspace based face recognition techniques. The second part presents one of the efficient interval-valued subspace techniques, namely, symbolic Kernel Fisher Discriminant analysis (Symbolic KFD), in which the interval type features are extracted in contrast to classical subspace based techniques where single valued features are used for face representation and recognition.
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