Facial occlusions, due for example to sunglasses, hats, scarf, beards etc., can significantly affect the performance of any face recognition system. Unfortunately, the presence of facial occlusions is quite common in real-world applications especially when the individuals are not cooperative with the system such as in video surveillance scenarios. While there has been an enormous amount of research on face recognition under pose/illumination changes and image degradations, problems caused by occlusions are mostly overlooked. The focus of this paper is thus on facial occlusions, and particularly on how to improve the recognition of faces occluded by sunglasses and scarf. We propose an efficient approach which consists of first detecting the presence of scarf/sunglasses and then processing the nonoccluded facial regions only. The occlusion detection problem is approached using Gabor wavelets, PCA and support vector machines (SVM), while the recognition of the non-occluded facial part is performed using block-based local binary patterns.
Experiments on AR face database showed that the proposed method yields significant performance improvements compared to existing works for recognizing partially occluded and also nonoccluded faces. Furthermore, the performance of the proposed approach is also assessed under illumination and extreme facial expression changes, demonstrating interesting results.
