Illumination Robust Face Recognition Based on Image Fusion of Local Contrast Enhancement and Adaptive Smoothing

Abstract

Abstract. In this paper, a new face recognition method is proposed under variable illumination conditions. Firstly, the adaptive smoothing technique is improved according to a new conduction function and a parameter. And it can smooth face images well without enhancing edge effects. However, face images after smoothing are very white and their local contrast diminishes. Accordingly, we adopt the local contrast enhancement (LCE) to preprocess original images. And then, the improved adaptive smoothing (IAS) technology and LCE technology are used to fuse these images according to the technique based on orientation information measurement(OIM) in nonsubsampled Shearlet transform(NSST) domain. Experiments are done on the Yale B and CMU PIE face databases by using sparse representation based classification(SRC), and the results demonstrate that the proposed method has higher recognition rates in comparison to single preprocessing methods and some other illumination normalization methods, and it is robust even when the training set is single and under various lighting conditions.

Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

“…And its data value distribution is unbalanced. The local contrast for a pixel (m, n) with the luminance value I(m, n) is given by θ is a predefined threshold, I(m, n) indicates the mean luminance value of the neighborhoods of the pixel (m, n) [32].…”