NDLI: Illumination Invariant Face Recognition System using Local Directional Pattern and Principal Component Analysis

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Illumination Invariant Face Recognition System using Local Directional Pattern and Principal Component Analysis

Content Provider	Semantic Scholar
Author	Latha B.
Copyright Year	2014
Abstract	In this paper, we propose an illumination-robust face recognition system using local directional pattern images. Usually, local pattern descriptors including local binary pattern and local directional pattern have been used in the field of the face recognition and facial expression recognition, since local pattern descriptors have important properties to be robust against the illumination changes and computational simplicity. Thus, this paper represents the face recognition approach that employs the local directional pattern descriptor and two-dimensional principal analysis algorithms to achieve enhanced recognition accuracy. In particular, we propose a novel methodology that utilizes the transformed image obtained from local directional pattern descriptor as the direct input image of two-dimensional principal analysis algorithms, unlike that most of previous works employed the local pattern descriptors to acquire the histogram features. The performance evaluation of proposed system was performed using well-known approaches such as principal component analysis and Gabor-wavelets based on local binary pattern, and publicly available databases including the Yale B database and the CMU-PIE database were employed.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ijcat.com/archives/volume3/issue12/ijcatr03121003.pdf
Alternate Webpage(s)	http://ijcat.com/archives/volume3/issue12/ijcatr03121003.pdf
Language	English
Access Restriction	Open
Subject Keyword	Algorithm Binary pattern (image generation) Computation Facial recognition system Gabor wavelet Histogram Pattern recognition Performance Evaluation Principal component analysis Published Database
Content Type	Text
Resource Type	Article

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