Hierarchical-PEP Model for Real-World Face Recognition

Haoxiang Li, Gang Hua; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4055-4064

Abstract


Pose variation remains one of the major factors adversely affect the accuracy of real-world face recognition systems. Inspired by the recently proposed probabilistic elastic part (PEP) model and the success of the deep hierarchical architecture in a number of visual tasks, we propose the Hierarchical-PEP model to approach the unconstrained face recognition problem. We apply the PEP model hierarchically to decompose a face image into face parts at different levels of details to build pose-invariant part-based face representations. Following the hierarchy from bottom-up, we stack the face part representations at each layer, discriminatively reduce its dimensionality, and hence aggregate the face part representations layer-by-layer to build a compact and invariant face representation. The Hierarchical-PEP model exploits the fine-grained structures of the face parts at different levels of details to address the pose variations. It is also guided by supervised information in constructing the face part/face representations. We empirically verify the Hierarchical-PEP model on two public benchmarks (i.e., the LFW and YouTube Faces) and a face recognition challenge (i.e., the PaSC grand challenge) for image-based and video-based face verification. The state-of-the-art performance demonstrates the potential of our method.

Related Material


[pdf]
[bibtex]
@InProceedings{Li_2015_CVPR,
author = {Li, Haoxiang and Hua, Gang},
title = {Hierarchical-PEP Model for Real-World Face Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}