Face Recognition From Single Sample Per Person by Learning of Generic Discriminant Vectors
نویسندگان
چکیده
منابع مشابه
Face Recognition From Single Sample Per Person by Learning of Generic Discriminant Vectors
The conventional ways of recognizing faces always assume the possession and heavily relies on extensive and representative datasets, but that is not the case in most real-world situations where more often than not, a very limited or even only single sample per person (SSPP) is available which ultimately rendering most face recognition systems to fail severely. This paper proposes a development ...
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As one of the most visible applications in computer vision communication, face recognition (FR) has become significant role in the community. In the past decade, researchers have been devoting themselves to addressing the various problems emerging in practical FR applications in uncontrolled or less controlled environment. In many practical applications of FR (e.g., law enforcement, e-passport,...
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Fisher linear discriminant analysis (LDA) is one of the most popular projection techniques for feature extraction and has been widely applied in face recognition. However, it cannot be used when encountering the single sample per person problem (SSPP) because the intra-class variations cannot be evaluated. In this paper, we propose a novel method coined local similarity based linear discriminan...
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ژورنال
عنوان ژورنال: Procedia Engineering
سال: 2012
ISSN: 1877-7058
DOI: 10.1016/j.proeng.2012.07.199