Probabilistic Facial Trait Code for face recognition

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.