Detect Faces Efficiently: A Survey and Evaluations

Author:

Feng Yuantao1,Yu Shiqi1ORCID,Peng Hanyang1,Li Yan-Ran2,Zhang Jianguo1ORCID

Affiliation:

1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Stable Support Plan Program of Shenzhen Natural Science Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Reference91 articles.

1. Group Sampling for Scale Invariant Face Detection

2. Seeing small faces from robust anchor’s perspective;zhu;Proc IEEE Conf Comput Vis Pattern Recognit (CVPR),2018

3. Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A

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