Abstract
Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.
Highlights
Many objective factors restrict the development of face recognition (FR) and facial expression recognition (FER) systems, such as face posture, illumination variation, and so on
We test and verify our proposed method by several experiments on gray face images from the extended Yale face database B [39, 40] and color face images from CMU-PIE [41], since the two face databases are commonly used to evaluate the performance of illumination normalization
As our work focus on illumination normalization, only frontal face images without variations in head pose are considered and resized to 394×326 pixels
Summary
Many objective factors restrict the development of face recognition (FR) and facial expression recognition (FER) systems, such as face posture, illumination variation, and so on. Some works [1, 2] have pointed out that the changes caused by the variation of illumination could be more significant than the differences between individual’s physical appearance. Some researchers [3] affirm that the variation of illumination could bring more negative influence to FR comparing with the pose and expression. Removing the negative effects of illumination over FR and FER is a challenging problem in image processing which generates intensive. Illumination Normalization of Face Image collection and analysis, decision to publish, or preparation of the manuscript
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