Robust Face Recognition Using Probabilistic Facial Trait Code

Recently, Facial Trait Code (FTC) was proposed for solving face recognition, and was reported with promising recognition rates. However, several simplifications in the FTC encoding make it unable to handle the most rigorous face recognition scenario in which only one facial image per individual is available for enrollment in the gallery set and the probe set includes faces under variations caused by illumination, expression, pose or misalignment. In this study, we propose the Probabilistic Facial Trait Code (PFTC) with a novel encoding scheme and a probabilistic codeword distance measure. We also proposed the Pattern-Specific Subspace Learning (PSSL) scheme that encodes and recognizes faces robustly under aforementioned variations. 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. Our experimental study considered factors such as the number of enrollment allowed in the gallery, the variation among gallery or probe set, and reported results for both identification and verification problems. The proposed PFTC yielded significant better recognition rates in most of the scenarios than all the states-of-the-art algorithms evaluated in this study.