Single-Sample Face Recognition via Fusion Variant Dictionary

This paper presents a novel method called sparse representation based classification via fusion variant dictionary (FSRC) for single-sample face recognition. There are two points to be highlighted in our method: (1) A specific preprocessing step is introduced to help the gray level of the testing sample distributed uniformly. (2) A fusion variant dictionary is proposed including two parts: the first part is an intra-class variant term, which can help represent the moderate illuminations, expressions and disguises; the second part is a noise term, which can help remove the common noise (caused by pixel noise, severe illumination or our preprocessing step) in testing samples. Extensive experiments on public face databases demonstrate advantages of the proposed method over the state-of-the-art methods, especially in dealing with image corruption and severe illumination.