Discriminative low-rank metric learning for face recognition

Metric learning has attracted increasing attentions recently, because of its promising performance in many visual analysis applications. General supervised metric learning methods are designed to learn a discriminative metric that can pull all the within-class data points close enough, while pushing all the data points with different class labels far away. In this paper, we propose a Discriminative Low-rank Metric Learning method (DLML), where the metric matrix and data representation coefficients are both constrained to be low-rank. Therefore, our approach can not only dig out the redundant features with a low-rank metric, but also discover the global data structure by seeking a low-rank representation. Furthermore, we introduce a supervised regularizer to preserve more discriminative information. Different from traditional metric learning methods, our approach aims to seek low-rank metric matrix and low-rank representation in a discriminative low-dimensional subspace at the same time. Two scenarios of experiments, (e.g. face verification and face identification) are conducted to evaluate our algorithm. Experimental results on two challenging face datasets, e.g. CMU-PIE face dataset and Labeled Faces in the Wild (LFW), reveal the effectiveness of our proposed method by comparing with other metric learning algorithms.