vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff195b130000002959010001000700 Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE), Gamma Intensity Correction (GIC), Normalization chain and Modified Homomorphic Filtering (MHF) are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Local Texture Pattern (LTP) and Local Tetra Patterns (LTrPs) are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.
K. Meena1, A. Suruliandi2, R. Reena Rose3
J. P. College of Engineering, India1, Manonmaniam Sundaranar University, India2, St. Xavier’s Catholic College of Engineering, India3
Face Recognition, Texture Analysis, Texture Features
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 4 , Issue: 2 , Pages: 709-716 )
Date of Publication :
November 2013
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