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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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. [doi:10.1109/ISDA.2006.253763]
Dai, J., Feng, J., Zhou, J., 2012. Robust and efficient ridge-based palmprint matching. IEEE Trans. Pattern Anal. Mach. Intell., 34(8):1618–1632. [doi:10.1109/TPAMI.2011.237]
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. [doi:10.1109/CVPRW.2010.5543236]
Kanhangad, V., Kumar, A., Zhang, D., 2011a. Contactless and pose invariant biometric identification using hand surface. IEEE Trans. Image Process., 20(5):1415–1424. [doi:10.1109/TIP.2010.2090888]
Kanhangad, V., Kumar, A., Zhang, D., 2011b. A unified framework for contactless hand verification. IEEE Trans. Inform. Forens. Secur., 6(3):1014–1027. [doi:10.1109/TIFS.2011.2121062]
Kong, A., Zhang, D., 2004. Competitive coding scheme for palmprint verification. Proc. 17th Int. Conf. on Pattern Recognition, p.520–523. [doi:10.1109/ICPR.2004.1334184]
Kumar, A., Zhang, D., 2007. Hand geometry recognition using entropy-based discretization. IEEE Trans. Inform. Forens. Secur., 2(2):181–187. [doi:10.1109/TIFS.2007.896915]
Malassiotis, S., Aifanti, N., Strintzis, M.G., 2006. Personal authentication using 3-D finger geometry. IEEE Trans. Inform. Forens. Secur., 1(1):12–21. [doi:10.1109/TIFS.2005.863508]
Methani, C., Namboodiri, A.M., 2009. Pose invariant palmprint recognition. LNCS, 5558:577–586. [doi:10.1007/978-3-642-01793-3_59]
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. [doi:10.1016/j.jvcir.2012.07.004]
Morales, A., Ferrer, M.A., Diaz, F., et al., 2008. Contactfree hand biometric system for real environments. 16th European Signal Processing Conf., p.1–5.
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. [doi:10.1109/CCST.2012.6393563]
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. [doi:10.1109/ICIP.2011.6115820]
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. [doi:10.1109/TPAMI.2005.209]
Sanchez-Reillo, R., 2000. Hand geometry pattern recognition through Gaussian mixture modelling. Proc. 15th Int. Conf. on Pattern Recognition, p.937–940. [doi:10.1109/ICPR.2000.906228]
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. [doi:10.1109/34.879796]
Wang, C., Liu, H., Liu, X., 2013. Maximally stable curvature regions for 3D hand tracking. IEEE Int. Conf. on Image Processing, p.3895–3899. [doi:10.1109/ICIP.2013.6738802]
Woodard, D., Flynn, P., 2005. Finger surface as a biometric identifier. Comput. Vis. Image Understand., 100(3):357–384. [doi:10.1016/j.cviu.2005.06.003]
Xiong, W., Toh, K., Yau, W., et al., 2005. Modelguided deformable hand shape recognition without positioning aids. Pattern Recogn., 38(10):1651–1664. [doi:10.1016/j.patcog.2004.07.008]
Zhang, D., Kong, W., You, J., et al., 2003. Online palmprint identification. IEEE Trans. Pattern Anal. Mach. Intell., 25(9):1041–1050. [doi:10.1109/TPAMI.2003.1227981]
Author information
Authors and Affiliations
Corresponding author
Additional information
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), and the Specialized Research Fund for the Doctoral Program of Higher Education, China (No. 20130001110011)
Rights and permissions
About this article
Cite this article
Wang, C., Liu, H. & Liu, X. Contact-free and pose-invariant hand-biometric-based personal identification system using RGB and depth data. J. Zhejiang Univ. - Sci. C 15, 525–536 (2014). https://doi.org/10.1631/jzus.C1300190
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.C1300190