Low-Light Face Image Enhancement Based on Dynamic Face Part Selection

摘要

A common challenge faced by face recognition community is struggling to circumvent face images that are acquired under low-light situation. The present work aims to couple the power of the popular CLAHE algorithm for face preprocessing with a Fuzzy inference system in such a way to correct the annoyance of non-uniform illumination of face images in a targeted and a precise manner. Due to the particularity of the low-light illumination problem. Firstly, the input face image is divided into two equal sub-regions. Subsequently, the degree of brightness in each sub-region and in the whole face is used for dynamic decision of whether to normalize. In the case where only one region of the face undertakes the CLAHE-Fuzzy approach is applied. Thus, the left and right face regions are grouped back followed by further processing like a blur removal and contrast enhancement (smoothing). Visual results showed that more facial features appeared in comparison with other approaches for enhancement. Besides, we quantitatively validate the accuracy of the developed Partial Fuzzy Enhancement Approach (PFEA) with four different metrics. The effectiveness of PFEA technique has been demonstrated by presenting extensive experimental results using Extended Yale-B, CMU-PIE, Mobio, and CAS-PEAL databases.