Illumination Compensation Using Oriented Local Histogram Equalization and its Application to Face Recognition

Abstract

Illumination compensation and normalization play a crucial role in face recognition. The existing algorithms either compensated low-frequency illumination, or captured high-frequency edges. However, the orientations of edges were not well exploited. In this paper, we propose the orientated local histogram equalization (OLHE) in brief, which compensates illumination while encoding rich information on the edge orientations. We claim that edge orientation is useful for face recognition. Three OLHE feature combination schemes were proposed for face recognition: 1) encoded most edge orientations; 2) more compact with good edge-preserving capability; and 3) performed exceptionally well when extreme lighting conditions occurred. The proposed algorithm yielded state-of-the-art performance on AR, CMU PIE, and extended Yale B using standard protocols. We further evaluated the average performance of the proposed algorithm when the images lighted differently were observed, and the proposed algorithm yielded the promising results.

Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters

“…Future work is to investigate other aspects of face image quality such as facial expression, pose, and occlusion, such quality measures are used in fully adaptive face recognition system, wh ich will select the most suitable gallery images, an appropriate face feature representation, and classification algorith m for given probe image and then able to predict the confidence of the system. Ping-Han Lee et al, [11] proposed orientated local histogram equalization (OLHE) technique, that compensates illu mination by encoding more informat ion on the edge orientations and argued that edge orientation is useful for face recognition. Three OLHE feature co mbination methods are proposed for face recognition: one encoded most edge orientations; one was more co mpact with good edge-preserving capability, the performed well when ext reme lighting conditions occur.…”

Illumination Insensitive Face Representation for Face Recognition Based on Modified Weberface

“…On the other hand, the oriented patterns have already had good applications to the face recognition in the literature. For instance, the oriented facial information was also considered in [17] and a new method called oriented local histogram equalization (OLHE) was successfully developed. It was applied to maintain facial features while compensating varying illumination.…”

Improvement of Illumination-Insensitive Feathers for Face Recognition under Complex Illumination Conditions

“…One approach deals with image processing modeling techniques, which are helpful to normalize faces with different lighting effects, such as logarithm transforms (LT) [3], gamma correction (GC) [4], block-based histogram equalization (BHE) [5], adaptive histogram equalization (AHE) [6], oriented local histogram equalization (OLHE) [7], the small-and large-scale (S&L) features [8] and enhanced local texture feature [9]. LT and GC are of low computational complexity due to global techniques of image enhancement, but uneven illumination variation is too tricky even by using these global processing methods.…”