Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data

作     者：Can WANG Hong LIU Xing LIU 

作者机构：Engineering Laboratory on Intelligent Perception for Internet of Things (ELIP) and MOE Key Laboratory of Machine Perception Shenzhen Graduate School Peking University 

出 版 物：《Journal of Zhejiang University-Science C(Computers and Electronics)》 (浙江大学学报C辑（计算机与电子（英文版）)

年 卷 期：2014年第15卷第7期

页      面：525-536页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080203[工学-机械设计及理论] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the National Natural Science Foundation of China(Nos.61340046,60875050,and 60675025) the National High-Tech R&D Program(863)of China(No.2006AA04Z247) the Scientific and Technical Innovation Commission of Shenzhen Municipality(Nos.JCYJ20120614152234873,CXC201104210010A,JCYJ20130331144631730,and JCYJ20130331144716089) the Specialized Research Fund for the Doctoral Program of Higher Education,China(No.20130001110011) 

主　　题：Hand biometric Contact free Pose invariant Identification system Multiple features 

摘      要：Hand-biometric-based personal identification is considered to be an effective method for automatic recognition. However, existing systems require strict constraints during data acquisition, such as costly devices,specified postures, simple background, and stable illumination. In this paper, a contactless personal identification system is proposed based on matching hand geometry features and color features. An inexpensive Kinect sensor is used to acquire depth and color images of the hand. During image acquisition, no pegs or surfaces are used to constrain hand position or posture. We segment the hand from the background through depth images through a process which is insensitive to illumination and background. Then finger orientations and landmark points, like finger tips or finger valleys, are obtained by geodesic hand contour analysis. Geometric features are extracted from depth images and palmprint features from intensity images. In previous systems, hand features like finger length and width are normalized, which results in the loss of the original geometric features. In our system, we transform 2D image points into real world coordinates, so that the geometric features remain invariant to distance and perspective effects. Extensive experiments demonstrate that the proposed hand-biometric-based personal identification system is effective and robust in various practical situations.