Face Recognition in Unconstrained Environments A Deep Architecture on A Small Training Set

This paper investigates three approaches to the problem of identity recognition in real-world unconstrained environments. We describe a new and challenging face recognition dataset captured in a laboratory environment with no strong constraints on lighting, motion, or subject pose, orientation, distance, or facial expression. We then evaluate three approaches to identity recognition on this new dataset. We find that a deep neural network with stacked denoising auto-encoders significantly outperforms a standard feedforward neural network and a baseline eigenfaces approach from the OpenCV library. Despite the 66 million plus parameters in the best trained deep network, it significantly outperforms the other two methods even on the relatively small number (relative to the number of deep network parameters) of 8,895 training samples. We believe our work adds to the growing empirical and theoretical evidence that deep networks provide a promising approach to unconstrained recognition problems.