A Robust Face Recognition Method for Occluded and Low-Resolution Images

Abstract

Face images that appear in multimedia applications, such as digital entertainments usually exhibit dramatic nonuniform illumination, occlusions, low-resolution, and pose/expression variations that result in substantial performance degradation for traditional face recognition algorithms. Recent research is focused to develop robust face recognition algorithms to solve the aforementioned issues with maximum effort to mimic the human vision system. This paper presents a near real-time and novel face recognition method to recognize the occluded and low-resolution face images. Proposed face recognition algorithm initially uses 68 points to locate a face in the input image. Meanwhile, the adaptive boosting and Linear Discriminant Analysis (LDA) are used to extract face features. In the final stage, classic nearest centre classifier is used for face classification. Detailed experiments are performed on two publicly available LFW and the AR databases. Simulation results reveal that the proposed method outperforms recent state-of-the-art face recognition algorithms by producing high recognition rate.