[1]林峰,杨忠程,冯英,等.利用场景光照识别优化的双目活体检测方法[J].智能系统学报,2020,15(1):160-165.[doi:10.11992/tis.201912026]
 LIN Feng,YANG Zhongcheng,FENG Ying,et al.Binocular camera based face liveness detection with optimized scene illumination recognition[J].CAAI Transactions on Intelligent Systems,2020,15(1):160-165.[doi:10.11992/tis.201912026]
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利用场景光照识别优化的双目活体检测方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年1期
页码:
160-165
栏目:
人工智能院长论坛
出版日期:
2020-01-01

文章信息/Info

Title:
Binocular camera based face liveness detection with optimized scene illumination recognition
作者:
林峰1 杨忠程2 冯英2 颜水成2 魏子昆2
1. 贵阳市信捷科技有限公司, 贵州 贵阳 550081;
2. 上海依图网络科技有限公司, 上海 200051
Author(s):
LIN Feng1 YANG Zhongcheng2 FENG Ying2 YAN Shuicheng2 WEI Zikun2
1. Guiyang Xinjie Technology Co., Ltd., Guiyang 550081, China;
2. YITU Tech, Shanghai 200051, China
关键词:
人脸活体检测人脸防伪展示攻击检测身份识别生物识别安全深度学习卷积神经网络PID控制
Keywords:
face liveness detectionface anti-counterfeitingdisplay attack detectionidentity recognitionbiometric securitydeep learningconvolutional neural networkPID control
分类号:
TP391
DOI:
10.11992/tis.201912026
摘要:
人脸识别是生物特征识别技术中应用最广的技术之一。其中,能判断人脸图像是否是真实人脸的活体检测模块,是系统安全运行的重要保障。目前从安全度和经济性两方面综合考虑,最常用的活体检测方法是双目活体检测。但由于不同场景下光线亮度和角度变化很大,拍摄的人脸图片质量参差不齐,严重影响了活体检测的质量。针对这一问题,提出了通过对场景光照识别进行优化从而提升检测准确度的双目活体识别算法。算法通过串级PID算法对摄像头的感光度和补光灯进行控制,并利用人脸识别算法定位优化测光区域,从而对不同的光线强度和角度采取不同的策略。经过实验验证:本方法将活体检测在复杂场景下的准确率提升约30%,保证了算法在室内外不同光照场景下的有效性。
Abstract:
Face recognition is one of the most widely applied biometric identification technologies, in which face liveness detection aiming to determine whether a face is genuine or fake, is used to help face recognition systems defend against replay and print attacks, and thus ensure system security. Considering safety and economy, binocular camera based face liveness detection is most commonly adopted at present. However, due to significant variations in lighting conditions of different scenes as well as face poses, the captured face images are often of low quality, which greatly harms the performance of face liveness detection. In this paper, we propose a binocular camera based face liveness detection algorithm, which improves detection performance through optimizing scene illumination recognition. In particular, the proposed algorithm uses the cascaded PID algorithm to adjust the light sensitivity and light supplement of the camera subject to specific lighting and pose angles. It also modifies the photometric range to be within the face area in the case of backlight to ensure effectiveness of the light exposure and supplement control strategy. Extensive experiments have been conducted and the results show that the proposed model outperforms other methods by around 30% in accuracy in complex scenes, with ensured generalizability to diverse application scenes.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-12-20。
作者简介:林峰,工程师,主要研究方向为大数据产业;杨忠程,高级研究员,主要研究方向为机器视觉;颜水成,新加坡工程院院士,IEEE Fellow, IAPR Fellow , ACM 杰出科学家,主要研究方向是计算机视觉、机器学习与多媒体分析。曾任360集团副总裁、首席科学家、人工智能研究院创始院长,现任依图科技CTO。
通讯作者:杨忠程.E-mail:zhongcheng.yang@yitu-inc.com
更新日期/Last Update: 1900-01-01