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Volume: 32 | Article ID: art00006
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A Review and Quantitative Evaluation of Small Face Detectors in Deep Learning
  DOI :  10.2352/ISSN.2470-1173.2020.6.IRIACV-048  Published OnlineJanuary 2020
Abstract

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.

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Hua Wu, Shuang Yang, Weihua Xiong, Shanhu Yu,Xinsheng Sun, Tongqi Wei, "A Review and Quantitative Evaluation of Small Face Detectors in Deep Learningin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2020,  pp 48-1 - 48-8,  https://doi.org/10.2352/ISSN.2470-1173.2020.6.IRIACV-048

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