Facial Age Estimation Based on Structured Low-rank Representation

This paper presents an algorithm based on structured, lowrank representation for facial age estimation. The proposed method learns the discriminative feature representation of images with the constraint of the classwise block-diagonal structure to promote discrimination of representations for robust recognition. A block-sparse regularizer is introduced to exploit the similarity and structure information of class. Based on the new representation, we estimate the accurate age using a regression function. By subtly introducing the structured, low-rank representation, we achieve good age estimation performance. Experimental results on three wellknown aging faces datasets have demonstrated that the proposed method is superior to the conventional approaches.