research-article

Research on Face Detection Based on FeatherNet

Authors:

Zhiyong Liu,

FuQiang Wang,

Xiaoming WuAuthors Info & Claims

DMIP '20: Proceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing

Pages 51 - 55

https://doi.org/10.1145/3441369.3441378

Published: 24 March 2021 Publication History

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References

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Howard, Andrew G, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” (2017)

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Chen, Sheng, “MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices,” 13th Chinese Conference, CCBR 2018, Urumqi, China, August 11-12, 2018, Proceedings. Biometric Recognition. Springer, Cham, 2018.

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P. Zhang, “FeatherNets: Convolutional neural networks as light as feather for face anti-spoofing,” Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), pp. 1574-1583, Jun. 2019.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

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Kaziakhmedov, Edgar, “Real-world attack on MTCNN face detection system,” (2019).

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DMIP '20: Proceedings of the 2020 3rd International Conference on Digital Medicine and Image Processing

November 2020

80 pages

ISBN:9781450389044

DOI:10.1145/3441369

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Published: 24 March 2021

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DMIP '20: 2020 3rd International Conference on Digital Medicine and Image Processing

November 6 - 9, 2020

Kyoto, Japan

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Range Face Segmentation: Face Detection and Segmentation for Authentication in Mobile Device Range Images

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Face Detection with Improved Face R-CNN Training Method

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