In this paper, a novel method called discriminative histogram intersection metric leing (DHIML) is proposed for pair matching and classification. Specifically, we introduce a discrimination term for leing a metric from binary infor-mation such as same/not-same or similar/dissimilar, and then combine it with the classification error for the discrimination in classifier construction. Compared with conventional approaches, the proposed method has several advantages. 1) The histogram intersection strategy is adopted into metric leing to deal with the widely used histogram features effectively. 2) By introducing discriminative term and classification error term into metric leing, a more discriminative distance metric and a classifier can be leed together. 3) The objective function is robust to outliers and noises for both features and labels in the training. The performance of the proposed method is tested on four applications: face verification, face-track identification, face-track clustering, and image classification. Evaluations on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark, a dataset with more than 7000 face-tracks, and Caltech-101 dataset validate the robustness and discriminability of the proposed metric leing, compared with the recent state-of-the-art approaches.