Enhancing Gray Scale Images for Face Detection under Unstable Lighting Condition

Abstract

Facial expression plays a vital role in no verbal communication between human beings. The brain, in a quarter of second, can determine the state of mind and the behaviour of a person using different traits in a stable lighting environment. This is not the case in real applications such as online learning or driver monitoring system where lighting is not stable. It is therefore important to study and improve performance of some image enhancement techniques on face detection under varying lighting conditions in the spatial domain. The study is based on gray scale images. Nine gray scale standards based on colour space separating luminance to other colour components are used. The enhancement techniques compared are: the Global Histogram Equalisation (GHE), the Adaptive Histogram Equalisation (AHE) and Contrast Limited Adaptive Histogram Equalisation (CLAHE). Trials on the Labelled Face in the Wild (LFW) dataset using the Viola Jones Haar like features showed the CLAHE to outperform the GHE and AHE in face detection though the results appeared poor under low lighting condition. This motivated the need to stabilize lighting before applying Histogram Equalization techniques. The novelty in this research is that we have been able to apply the Gamma transform as a lighting stabiliser on the gray scale standard before enhancement. Comparing performance after lighting stabilisation showed AHE to be most appropriate for face detection, as it produced a detection rate of 99.31% and a relative high false positive rate (23.89 %).