Similarity Metric Learning for Face Recognition

Qiong Cao, Yiming Ying, Peng Li; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2408-2415


Recently, there is a considerable amount of efforts devoted to the problem of unconstrained face verification, where the task is to predict whether pairs of images are from the same person or not. This problem is challenging and difficult due to the large variations in face images. In this paper, we develop a novel regularization framework to learn similarity metrics for unconstrained face verification. We formulate its objective function by incorporating the robustness to the large intra-personal variations and the discriminative power of novel similarity metrics. In addition, our formulation is a convex optimization problem which guarantees the existence of its global solution. Experiments show that our proposed method achieves the state-of-the-art results on the challenging Labeled Faces in the Wild (LFW) database [10].

Related Material

author = {Cao, Qiong and Ying, Yiming and Li, Peng},
title = {Similarity Metric Learning for Face Recognition},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}