A Review and Quantitative Evaluation of Small Face Detectors in Deep Learning

• Published in: Electronic Imaging Vol. 32; no. 6; pp. 48-1 - 48-8
• Main Authors: Wu, Hua, Yang, Shuang, Xiong, Weihua, Sun, Shanhu Yu,Xinsheng, Wei, Tongqi
• Format: Journal Article
• Language: English
• Published: 7003 Kilworth Lane, Springfield, VA 22151 USA Society for Imaging Science and Technology 26.01.2020
• Subjects: Comparative Evaluation; Deep Learning; Review; Small Face Detector
• ISSN: 2470-1173; 2470-1173
• DOI: 10.2352/ISSN.2470-1173.2020.6.IRIACV-048

Face detection is crucial to computer vision and many similar applications. Past decades have witnessed great progress in solving this problem. Contrary to traditional methods, recently many researchers have proposed a variety of CNN(Convolutional Neural Network) methods and have given out impressive results in diverse ways. Although many comprehensive evaluations or reviews about face detection are available, very few focuses on small face detection strategies. In this paper, we systematically survey some of the prevailing methods; divide them into two categories and compare them qualitatively on three real-world image data sets in terms of mAP. The experimental results show that feature pyramid with multiple predictors can produce better performance, which is helpful in future direction of research work.