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Performance Analysis of Face Recognition Techniques for Feature Extraction

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Due to increasing applications which need security, face is the most natural way to recognize the person. This paper presents the performance analysis of different face recognition techniques. Linear Binary Pattern (LBP), Gabor Wavelet, Histogram of Oriented Gradient (HOG) techniques are used for feature extraction on two standard databases of images i.e., ORL and Yale Face database. Then Principal Component Analysis (PCA) is applied for feature selection with six different distance metric functions, Cosine, Euclidean, Correlation, Cityblock, Spearman and Minkowski for similarity matching of images.
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Keywords: Face Recognition; Gabor Wavelet; HOG; LBP; ORL and Yale Face Database; PCA

Document Type: Research Article

Affiliations: 1: MM Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala 133207, India 2: Department of Computer Science & Applications, Kurukshetra University, Kurukshetra 136119, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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