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计算机工程 ›› 2019, Vol. 45 ›› Issue (3): 256-261. doi: 10.19678/j.issn.1000-3428.0050353

• 多媒体技术及应用 • 上一篇    下一篇

面向VTM的交互式活体检测算法

马钰锡,谭励,董旭,于重重   

  1. 北京工商大学 计算机与信息工程学院 食品安全大数据技术北京市重点实验室,北京 100048
  • 收稿日期:2018-01-30 出版日期:2019-03-15 发布日期:2019-03-15
  • 作者简介:马钰锡(1995—),男,硕士研究生,主研方向为计算机视觉、人工智能;谭励,教授、博士;董旭,硕士;于重重,教授、博士。
  • 基金资助:

    国家自然科学基金(61702020);北京市自然科学基金(4172013)。

Interactive Liveness Detection Algorithm for VTM

MA Yuxi,TAN Li,DONG Xu,YU Chongchong   

  1. Beijing Key Laboratory of Big Data Technology for Food Safety,School of Computer and Information Engineering, Beijing Technology and Business University,Beijing 100048,China
  • Received:2018-01-30 Online:2019-03-15 Published:2019-03-15

摘要:

为提升远程视频柜员机人脸识别登录系统的识别率和安全性,将改进的眨眼检测、背景检测和随机组合动作指令相结合,提出一种交互式活体检测算法。基于OpenCV级联分类器人脸检测和局部二值特征人脸对齐算法,结合坐标比例和眼球色素变化改进眨眼检测。利用背景检测和随机组合动作指令抵御动态视频攻击,加入图像质量检测与校正功能,使系统在弱光、歪斜等环境影响下对活体人脸检测有较好的检测效果。在活体人脸数据库CASIA-FASD和自建样本库上进行实验,结果表明,该算法识别率达到97.67%,与多光谱、卷积神经网络等检测算法相比性能有明显的提升。

关键词: 活体检测, 人脸识别, 眨眼检测, 背景检测, 局部二值特征

Abstract:

In order to improve the recognition rate and security of the Video Teller Machine(VTM) face recognition login system,an interactive liveness detection algorithm that combines improved blink detection,background detection and random combined action instructions is proposed.Based on the OpenCV cascade classifier face detection and Local Binary Feature(LBF) face alignment algorithm,combining the coordinate proportion and the eye pigment change,the detection method is improved.Uses the background detection and the random combination action instruction to resist the dynamic video attack.Making use of the image quality detection and correction function,the system in weak light,skew and the other environmental condition has a good performance as well.Experiments are carried out on liveness face database CASIA-FASD and self-built sample library,the result shows that the recognition rate reaches 97.67%,which is obviously improved than multispectral,convolutional neural network,and the other existing detection algorithms.

Key words: liveness detection, face recognition, blink detection, background detection, Local Binary Feature(LBF)

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