  Sparse representation-based classification (SRC) has been shown to achieve a
high level of accuracy in face recognition (FR). However, matching faces
captured in unconstrained video against a gallery with a single reference
facial still per individual typically yields low accuracy. For improved
robustness to intra-class variations, SRC techniques for FR have recently been
extended to incorporate variational information from an external generic set
into an auxiliary dictionary. Despite their success in handling linear
variations, non-linear variations (e.g., pose and expressions) between probe
and reference facial images cannot be accurately reconstructed with a linear
combination of images in the gallery and auxiliary dictionaries because they do
not share the same type of variations. In order to account for non-linear
variations due to pose, a paired sparse representation model is introduced
allowing for joint use of variational information and synthetic face images.
The proposed model, called synthetic plus variational model, reconstructs a
probe image by jointly using (1) a variational dictionary and (2) a gallery
dictionary augmented with a set of synthetic images generated over a wide
diversity of pose angles. The augmented gallery dictionary is then encouraged
to pair the same sparsity pattern with the variational dictionary for similar
pose angles by solving a newly formulated simultaneous sparsity-based
optimization problem. Experimental results obtained on Chokepoint and COX-S2V
datasets, using different face representations, indicate that the proposed
approach can outperform state-of-the-art SRC-based methods for still-to-video
FR with a single sample per person.
