Makeup-robust face verification

We investigate in this paper the problem of face verification in the presence of face makeups. To our knowledge, this problem has less formally addressed in the literature. A key challenge is how to increase the measured similarity between face images of the same person without and with makeups. In this paper, we propose a novel approach for makeup-robust face verification, by measuring correlations between face images in a meta subspace. The meta subspace is learned using canonical correlation analysis (CCA), with the objective that intra-personal sample correlations are maximized. Subsequently, discriminative learning with the support vector machine (SVM) classifier is applied to verify faces based on the low-dimensional features in the learned meta subspace. Experimental results on our dataset are presented to demonstrate the efficacy of our approach.