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.

View PDF No PDF? Try Arxiv!