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High Accurate Low Complex Face Detection Based on KL Transform and YCBCR Gaussian Model


Affiliations
1 Department of Electronics and Communication Engineering, VIT University, India
     

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This paper presents a skin color model for face detection based on YCbCr Gauss model and KL transform. The simple gauss model and the region model of the skin color are designed in both KL color space and YCbCr space according to clustering. Skin regions are segmented using optimal threshold value obtained from adaptive algorithm. The segmentation results are then used to eliminate likely skin region in the gauss-likelihood image. Different morphological processes are then used to eliminate noise from binary image. In order to locate the face, the obtained regions are grouped out with simple detection algorithms. The proposed algorithm works well for complex background and many faces.

Keywords

KL Transform, Gauss Model, Statistical Model, Morphological Processing.
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  • High Accurate Low Complex Face Detection Based on KL Transform and YCBCR Gaussian Model

Abstract Views: 203  |  PDF Views: 0

Authors

Epuru Nithish Kumar
Department of Electronics and Communication Engineering, VIT University, India
Gaddam Manoj Kumar
Department of Electronics and Communication Engineering, VIT University, India
Gunda Venkatesh
Department of Electronics and Communication Engineering, VIT University, India
Tanguturi Sai Jaswanth
Department of Electronics and Communication Engineering, VIT University, India
R. Menaka
Department of Electronics and Communication Engineering, VIT University, India

Abstract


This paper presents a skin color model for face detection based on YCbCr Gauss model and KL transform. The simple gauss model and the region model of the skin color are designed in both KL color space and YCbCr space according to clustering. Skin regions are segmented using optimal threshold value obtained from adaptive algorithm. The segmentation results are then used to eliminate likely skin region in the gauss-likelihood image. Different morphological processes are then used to eliminate noise from binary image. In order to locate the face, the obtained regions are grouped out with simple detection algorithms. The proposed algorithm works well for complex background and many faces.

Keywords


KL Transform, Gauss Model, Statistical Model, Morphological Processing.