Face Liveness Detection Algorithm based on Livenesslight Network

	Face Liveness Detection Algorithm based on Livenesslight Network
	Zuo, Yinlong 2; Gao, Wenlong 2; Wang JT(王金涛)1
	2020
会议日期	May 23, 2020
会议地点	Shenzhen, China
关键词	Deep Learning Face Liveness Detection CNN Network Light Structure
英文摘要	Face liveness detection is the first stage of the whole face detection technology, and it is of great significance to the system security using face recognition technology. In this paper we use convolutional neural networks to extract facial features. Compared to ResNet, SqueezeNet and VGG Face networks, the network structure is lighter and the model training takes less time. After testing, it achieved an accuracy of 99.5% on the Nanjing University of Aeronautics and Astronautics (NUAA) face detection data set of China Southern Airlines. In order to improve the robustness of the model, we built our own dataset through collecting pictures from internet and camera shooting, and horizontally compared the accuracy of the above several models on the self-built dataset. Finally the model proposed in this paper achieved the highest 99.02% accuracy and achieved the best results in actual testing.
源文献作者	Institute of Semicondutors, Chinese Academy of Sciences ; Professional Committee on Neural Networks and Computational Intelligence of Chinese Association for Artifical Intelligence ; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences ; Shenzhen International Robot City Industrial Park ; Shenzhen Longgang District Robotics Association
产权排序	2
会议录	2020 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2020
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-7281-6511-0
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/27713]
专题	沈阳自动化研究所_其他
通讯作者	Zuo, Yinlong
作者单位	1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 2.Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China
推荐引用方式 GB/T 7714	Zuo, Yinlong,Gao, Wenlong,Wang JT. Face Liveness Detection Algorithm based on Livenesslight Network[C]. 见:. Shenzhen, China. May 23, 2020.