While significant strides have been made in the recognition of faces under controlled viewing conditions, face recognition "in the wild" remains a challenging unsolved problem [12, 13, 21] . Interestingly, while humans are generally excellent at identifying familiar individuals under such conditions, their performance is significantly worse with unfamiliar individuals [5] and groups [19] , leading to the idea that brain may have enhanced or specialized representations of familiar individuals [6] . Inspired by these observations, we explored the use of a number of subspace analysis techniques, applied to various visual representations, to generate person-specific subspaces of "familiar" individuals for face identification. In particular, we introduce a person-specific application of partial least squares (PS-PLS) to generate per-individual subspaces, and show that operating in these subspaces yields state-of-the-art performance on the challenging PubFig83 familiar face identification benchmark. The results underscore the potential importance of incorporating a notion of familiarity into face recognition systems.
In REF , person-specific subspace analysis is presented for identification of celebrities.
Person-Specific Subspace Analysis for Unconstrained Familiar Face Identification.
BMVC
Computer Science