EPML: Expanded Parts Based Metric Learning for Occlusion Robust Face Verification

Abstract

We propose a novel Expanded Parts based Metric Learning (EPML) model for face verification. The model is capable of mining out the discriminative regions at the right locations and scales, for identity based matching of face images. It performs well in the presence of occlusions, by avoiding the occluded regions and selecting the next best visible regions. We show quantitatively, by experiments on the standard benchmark dataset Labeled Faces in the Wild (LFW), that the model works much better than the traditional method of face representation with metric learning, both (i) in the presence of heavy random occlusions and, (ii) also, in the case of focussed occlusions of discriminative face regions such as eyes or mouth. Further, we present qualitative results which demonstrate that the method is capable of ignoring the occluded regions while exploiting the visible ones.

Teaching Video Analytics Based on Student Spatial and Temporal Behavior Mining

“…[18] proposes a collaborative approach to improve the performance of face verification and human attribute learning. [12] proposes a novel Expanded Parts based Metric Learning (EPML) model for face verification. The model is able to mine the discriminative regions at the right locations and scales.…”