Work

From Facial Parts Responses to Face Detection: A Deep Learning Approach

Year: 2015

Type: conference-paper

Abstract: In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method [23] by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging cases where faces are only partially visible. This consideration allows our network to detect faces under severe occlusion and unconstrained pose variation, which are the main difficulty and bottleneck of most existing face detection approaches. We show that despite the use of DCN, our network can achieve practical runtime speed.

Source: IEEE/CVF International Conference on Computer Vision (ICCV)

DOI: 10.1109/iccv.2015.419

Authors: Shuo Yang, Ping Luo, Chen-Change Loy, Xiaoou Tang

Institutions: Chinese University of Hong Kong, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: 面向互联网图像视频的鲁棒人脸分析与识别技术研究, G4817798332