Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

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

Some examples of different illumination distributions.

<p>Reprinted from <a href="http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html" target="_blank">http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html</a> under a CC BY license, with permission from David Kriegman, original copyright 2001.</p

The schematic diagram for the rule of interception.

<p>The schematic diagram for the rule of interception.</p

The main steps of the illuminant direction estimation method.

<p>It should be noted that three of sixteen local regions are selected to calculate the final illuminant direction.</p

The location accuracies for different methods.

<p>(a) Gray image. (b) Color image.</p

The three-dimensional and two-dimensional representations of Gaussian function used in MSRCR.

<p>(a) No normalization. (b) Normalization.</p

Central region’s coordinates of Gaussian and their corresponding values.

<p>(a) Coordinate. (b) The distribution of conventional Gaussian’s values. (c) The real situation.</p

Performance comparisons of different methods.

<p>Performance comparisons of different methods.</p