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.

Recently, a new facial encoding scheme, namely Facial Trait Code (FTC), was proposed. FTC encoded human faces into a series of integers. Distances between codewords of different people were maximized during the code construction. FTC was applied to solve face recognition, and was reported with promising verification rates. However, due to several simplifications in the FTC encoding, its performance degraded considerably when there were only few images per individual available for enrollment in the gallery sets, or when the probe set included faces under large variations in illumination and expression. In this paper, we proposed the Probabilistic Facial Trait Code (PFTC) with a novel encoding scheme and a probabilistic codeword distance measure. The impact made by illumination and expression variations were also handled in the construction of PFTC. The proposed PFTC was evaluated and compared with state-of-the-art algorithms, including the FTC, the algorithm using sparse representation, and the one using Local Binary Pattern. PFTC out-performed the algorithms compared in this study in most scenarios.

This paper presents i-m-Space, an interactive multimedia rehabilitation space that helps the post-surgery recovery of breast cancer patients. Our goal is to improve patients' physical therapy and psychological relaxation experience through careful applications of multimedia technology. i-m-Space consists of three types of breathing-based relaxation and three types for interactive exercise-based rehabilitation.