应用色彩纹理特征的人脸防欺骗算法

doi:10.11896/jsjkx.180901688

计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 180-185.doi: 10.11896/jsjkx.180901688

应用色彩纹理特征的人脸防欺骗算法

包晓安¹, 林晓东¹, 张娜¹, 徐璐¹, 吴彪²

(浙江理工大学信息学院杭州310018)¹
(山口大学东亚研究科山口753-8514)²

收稿日期:2018-09-10 修回日期:2019-02-24 出版日期:2019-10-15 发布日期:2019-10-21
通讯作者: 张娜(1977-),女,硕士,副教授,主要研究方向为智能信息处理,E-mail:zhangna@zstu.edu.cn。
作者简介:包晓安(1973-),男,硕士,教授,主要研究方向为图像处理、机器学习;林晓东(1993-),男,硕士生,主要研究方向为图像处理、机器学习;徐璐(1988-),男,博士,讲师,主要研究方向为3D图像;吴彪(1989-),男,博士生,主要研究方向为图像识别。
基金资助:
本文受国家自然科学基金(61502430,61562015),广西自然科学重点基金(2015GXNSFDA139038),浙江理工大学521人才培养计划项目资助。

Face Anti-spoofing Detection Using Color Texture Feature

BAO Xiao-an¹, LIN Xiao-dong¹, ZHANG Na¹, XU Lu¹, WU Biao²

(School of Information Science and Technology,Zhejiang Sci-tech University,Hangzhou 310018,China)¹
(The Graduate School of East Asian Studies,Yamaguchi University,Yamaguchi-shi 753-8514,Japan)²

Received:2018-09-10 Revised:2019-02-24 Online:2019-10-15 Published:2019-10-21

摘要/Abstract

摘要： 针对目前人脸识别系统中存在易被人脸照片、人脸视频等方式攻击的问题,提出了一种应用融合色彩纹理特征的人脸防欺骗检测算法。目前,主要的人脸防欺骗检测算法分为用户配合式检测与静默式检测。针对如今火热的在线认证系统,静默式活体检测因具有良好的用户体验性以及分类结果的精确性,成为了该领域的热门研究方向。不同于当前静默式活体检测算法中较为流行的基于亮度特征以及图像质量分析的活体检测方法,文中在验证了色彩特征信息对区分活体人脸与虚假人脸的有效性的基础上,充分地研究了局部纹理特征的特性,并提出了一种结合亮度特征、色彩特征以及局部纹理特征的特征提取融合算法。首先,通过seetaFace人脸检测算法定位人脸及人眼坐标,并利用人眼坐标信息提取仅包含人脸的图像,以减少周围背景图像的干扰;其次,通过转换色彩空间的方式分离图像中的色彩信息和亮度信息,利用色道分离的方式有效地提取纹理特征;最后,采用融合局部纹理特征的提取方法在不同色道上提取特征,并将各通道提取的特征向量联合为一个特征向量,运用支持向量机(Support Vector Machine,SVM)训练分类器。将所提算法在MSU,CASIA,OULU标准人脸活体检测数据集中进行实验,实验结果表明,算法的性能良好,在分类准确率上取得了良好的效果。

关键词: 活体检测, 人脸识别, 纹理特征, 颜色空间

Abstract: Aiming at the difficulty that face recognition system could be easily deceived by face photos and face videos,a face anti-spoofing detection algorithm were proposed,which uses the fusion color texture features.At present,the main face anti-spoofing detection algorithms are divided into user-matched detection and silent detection.For hot online authentication system nowadays,silent detection has become popular because of its good user experience and accuracy of classification results.Different from the currently popular methods based on brightness characteristics and image quality analysis,the proposed method studies the effectiveness of color features and combined texture features,and then the method combining brightness features,color features and local texture features is proposed.Firstly,the seetaFace algorithm is used to get the coordinates of face and eyes.And then the images which only contain the face are extracted to reduce the interference of background.Secondly,the color information and the brightness information in the image are separated by converting color space and color channel separation.Finally,the method of extracting fusion local texture features is used to extract features from different channels and the feature vectors extracted by each channel are combined and stretched into one-D feature vector,and SVM(Support Vector Machine)is used to train the classifier.The algorithm was performed on the MSU,ASIA,ULU base-line spoofing face database.The experimental results show that the proposed method performs well on improving classification accuracy.

Key words: Color space, Face recognition, Liveness detection, Texture feature

中图分类号:

TP391.4

包晓安, 林晓东, 张娜, 徐璐, 吴彪. 应用色彩纹理特征的人脸防欺骗算法[J]. 计算机科学, 2019, 46(10): 180-185. https://doi.org/10.11896/jsjkx.180901688

BAO Xiao-an, LIN Xiao-dong, ZHANG Na, XU Lu, WU Biao. Face Anti-spoofing Detection Using Color Texture Feature[J]. Computer Science, 2019, 46(10): 180-185. https://doi.org/10.11896/jsjkx.180901688

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应用色彩纹理特征的人脸防欺骗算法

Face Anti-spoofing Detection Using Color Texture Feature

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