活泼
计算机科学
人工智能
面子(社会学概念)
计算机视觉
特征(语言学)
图像分辨率
图像(数学)
像素
模式识别(心理学)
信号(编程语言)
分辨率(逻辑)
目标检测
特征提取
字错误率
对象(语法)
理论计算机科学
社会科学
语言学
哲学
社会学
程序设计语言
作者
Qingtian Guan,Huaxia Deng,W Liang,Mingyang Ni,Xicheng Gao,Mengchao Ma,Xiang Zhong,Xinglong Gong
摘要
The security of liveness detection in face recognition is a crucial issue, but many attacks can spoof current face feature techniques. To enhance the security of liveness detection, a method is proposed to extract human physiological components from the object and classify the properties. The proposed method, different from traditional camera-based methods that require specific movement of the human face, separates the heart rate (HR) components from the computational ghost imaging (CGI) signal and achieves liveness detection by capturing only one image rather than image sequences. The correct rate reaches 96.0% against picture attacks and mask attacks. The average error is only 3.57% compared to commercial contact HR measuring devices. Meanwhile, this method is found resolution-independent and can work in low-resolution conditions, which is experimentally verified at a resolution of 32 × 32 pixels. This method can enhance the security of liveness detection and provide a fresh framework for physiological measurements.
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