Face recognition based on SIGMA sets of image features
Automatic face recognition is prevalent in a wide range of systems these days and it is critical to explore new techniques in order to enhance the state of the art. In this paper, we analyze the Region Covariance Matrix (RCM) and its enhancement based on Sigma sets as a feature extraction procedure for face images. The RCM features encode the covariance of various low level features, e.g., pixel intensities and gradients. Sigma sets, on the other hand, reduce the computational complexity of comparing two RCMs. Based on our experiments on the Labeled Faces in the Wild (LFW) dataset, we show that the proposed technique outperforms the popular Local Binary Patterns (LBP) technique and is on par with other better performing techniques that use complex classifiers.