Regularized metric adaptation for unconstrained face verification
Boyu Lu, Jun-Cheng Chen, Rama Chellappa · 2016
In this work, we propose a metric adaptation method for set-based face verification and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version, the Janus Challenging Set 2 (CS2). A template-specific metric is trained to adaptively learn the discriminative information in test templates and the negative training set, which contains subjects that are mutually exclusive to subjects in test templates. The proposed regularized joint Bayesian metric learning framework not only alleviates the over-fitting problem but also provides a way to efficiently reduce the model size. We also analyze the selection of the compact and representative negative set to speed up the training time and to reduce storage space. Experiments on the IJB-A and CS2 datasets yield promising results.