基于人脸的活体检测系统

人脸识别技术由于其成本低、用户友好、效率高等特点被广泛应用，同时也出现了针对人脸识别的身份伪造攻击，主要包括照片人脸攻击、视频人脸攻击、三维人脸模型攻击等方式，对于这些攻击方式的防范方法都是围绕着基于人脸的活体检测这个中点进行展开. 本文着重研究的活体检测方法为眨眼检测与背景分析算法，通过区域增长算法进行人眼定位、形态学操作进行人眼张合判断、感知Hash 算法进行背景差异对比，构造出一个复合的活体检测系统. 基于复合的眨眼检测与背景分析算法，本文设计了一个包含眨眼检测模块与背景分析模块的活体检测系统，使用OpenCV2.4.9 与vs2012 的MFC 架构实现了一个可以抵御照片攻击与视频攻击的活体检测系统，并对系统进行实验与评估，在与其它同类型的系统进行比较的结果来看，本文实现的系统性能表现优异.

Liveness Detection System Based on Human Face

The face recognition technology is widely used for its low cost, user-friendly and high efficiency. At the same time, identity forgery attack has also been the corresponding occurrence. The face recognition system attacks include photo face attacks, video face attacks and three-dimensional face model attacks, etc. For these attacks, prevention methods are carried out around the midpoint of in liveness detection based on human face. This paper focuses on the blink detection and background analysis algorithm, and carries out eye location with regional growth algorithm. The morphological operation is used to judge the human eye state, and the Hash algorithm is used to compose the background difference. These methods construct a Liveness Detection Systems. Based on the blink detection and background analysis algorithm, this paper designs a liveness detection system including blink detection module and background analysis module; uses the MFC architecture and OpenCV2.4.9 to build a liveness detection system which can resist photo attack and video attack; makes the experiment and evaluation of the system. In comparison with other similar types of systems, the system performance of this paper is excellent.