JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE C

Accepted manuscript available online (unedited version)

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

Author(s): Can Wang, Hong Liu, Xing Liu
Affiliation(s): Engineering Laboratory on Intelligent Perception for Internet of Things (ELIP) and MOE Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
Corresponding email(s): herowc@pku.edu.cn, hongliu@pku.edu.cn, liuxing@sz.pku.edu.cn
Key Words: Hand biometric, Contact free, Pose invariant, Identification system, Multiple features

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Can Wang, Hong Liu, Xing Liu. Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data[J]. Journal of Zhejiang University Science C,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.C1300190

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%A Can Wang
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%J Journal of Zhejiang University SCIENCE C
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: 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.

基于人手生物测量信息并利用颜色和深度数据的身份识别系统

研究目的：利用人手生物测量信息被认为是身份自动识别的一种有效方法。我们利用深度信息和颜色信息提取多种线索，以增加身份识别的精度。
创新要点：在已有方法基于颜色、纹理特征的基础上，使用深度传感器提供的深度信息，充分运用人手轮廓的曲率特征提取人手几何特征，并利用人手轮廓特征和手掌平面拟合对不同姿态的人手进行姿态统一化。
方法提亮：首先利用深度信息在图像分割上的鲁棒性提取人手区域，然后利用人手轮廓的几何信息矫正人手姿态。对于矫正后的人手区域，分别提取基于深度的几何特征和基于颜色和纹理的特征，并结合之前利用颜色信息提取人手生物信息的经典特征，描述人手的生物特征。充分利用了深度信息在人手姿态矫正上的优势和人手轮廓等集合信息。基于颜色和纹理的信息可用很多经典方法提到的特征，并可用高清相机采集颜色信息。
重要结论：大量实验证实，融合多种线索描述人手的生物特征，提升了基于传统特征提取人手特征和识别身份的精度，在实际应用中有效且鲁棒。
颜色和深度数据；RGB-D；生物测量；身份识别

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Reference

[1]Choras, R.S., Choras, M., 2006. Hand shape geometry and palmprint features for the personal identification. 6th Int. Conf. on Intelligent Systems Design and Applications, p.1085-1090.

[2]Dai, J., Feng, J., Zhou, J., 2012. Robust and efficient ridge-based palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell., 34(8):1618-1632.

[3]Kanhangad, V., Kumar, A., Zhang, D., 2010. Human hand identification with 3D hand pose variations. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, p.17-21.

[4]Kanhangad, V., Kumar, A., Zhang, D., 2011a. Contactless and pose invariant biometric identification using hand surface. IEEE Trans. Image Process., 20(5):1415-1424.

[5]Kanhangad, V., Kumar, A., Zhang, D., 2011b. A unified framework for contactless hand verification. IEEE Trans. Inform. Forens. Secur., 6(3):1014-1027.

[6]Kong, A., Zhang, D., 2004. Competitive coding scheme for palmprint verification. Proc. 17th Int. Conf. on Pattern Recognition, p.520-523.

[7]Kumar, A., Zhang, D., 2007. Hand geometry recognition using entropy-based discretization. IEEE Trans. Inform. Forens. Secur., 2(2):181-187.

[8]Malassiotis, S., Aifanti, N., Strintzis, M.G., 2006. Personal authentication using 3-D finger geometry. IEEE Trans. Inform. Forens. Secur., 1(1):12-21.

[9]Methani, C., Namboodiri, A.M., 2009. Pose invariant palmprint recognition. LNCS, 5558:577-586.

[10]Michael, G.K.O., Connie, T., Teoh, A.B.J., 2012. A contactless biometric system using multiple hand features. J. Vis. Commun. Image Represent., 23(7):1068-1084.

[11]Morales, A., Ferrer, M.A., Diaz, F., et al., 2008. Contact-free hand biometric system for real environments. 16th European Signal Processing Conf., p.1-5.

[12]Morales, A., Ferrer, M.A., Travieso, C.M., et al., 2012. Multisampling approach applied to contactless hand biometrics. IEEE Int. Carnahan Conf. on Security Technology, p.224-229.

[13]Ramalho, M., Correia, P., Soares, L., 2011. Distributed source coding for securing a hand-based biometric recognition system. 18th IEEE Int. Conf. on Image Processing, p.1825-1828.

[14]Ribaric, S., Fratric, I., 2005. A biometric identification system based on eigenpalm and eigenfinger features. IEEE Trans. Pattern Anal. Mach. Intell., 27(11):1698-1709.

[15]Sanchez-Reillo, R., 2000. Hand geometry pattern recognition through Gaussian mixture modelling. Proc. 15th Int. Conf. on Pattern Recognition, p.937-940.

[16]Sanchez-Reillo, R., Sanchez-Avila, C., Gonzalez-Macros, A., 2000. Biometric identification through hand geometry measurements. IEEE Trans. Pattern Anal. Mach. Intell., 22(10):1168-1171.

[17]Wang, C., Liu, H., Liu, X., 2013. Maximally stable curvature regions for 3D hand tracking. IEEE Int. Conf. on Image Processing, p.3895-3899.

[18]Woodard, D., Flynn, P., 2005. Finger surface as a biometric identifier. Comput. Vis. Image Understand., 100(3):357-384.

[19]Xiong, W., Toh, K., Yau, W., et al., 2005. Model-guided deformable hand shape recognition without positioning aids. Pattern Recogn., 38(10):1651-1664.

[20]Zhang, D., Kong, W., You, J., et al., 2003. Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell., 25(9):1041-1050.

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基于人手生物测量信息并利用颜色和深度数据的身份识别系统

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