Abstract
Hand biometrics, including fingerprint, palmprint, hand geometry and hand vein pattern, have obtained extensive attention in recent years. Physiologically, skin is a complex multi-layered tissue consisting of various types of components. Optical research suggests that different components appear when the skin is illuminated with light sources of different wavelengths. This motivates us to extend the capability of camera by integrating information from multispectral palm images to a composite representation that conveys richer and denser pattern for recognition. Besides, usability and security of the whole system might be boosted at the same time. In this paper, comparative study of several pixel level multispectral palm image fusion approaches is conducted and several well-established criteria are utilized as objective fusion quality evaluation measure. Among others, Curvelet transform is found to perform best in preserving discriminative patterns from multispectral palm images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2003)
Bolle, R., Pankanti, S., Jain, A.K.: Biometrics: Personal Identification in Networked Society. Springer, Heidelberg (1999)
Lin, C.-L., Fan, K.-C.: Biometric Verification Using Thermal Images of Palm-Dorsa Vein Patterns. IEEE Trans. on Circuits and Systems for Video Technology 14(2), 199–213 (2004)
Finger Vein Authentication Technology, http://www.hitachi.co.jp/Prod/comp/finger-vein/global/
Fujitsu Palm Vein Technology, http://www.fujitsu.com/global/about/rd/200506palm-vein.html
Igarashi, T., Nishino, K., Nayar, S.K.: The Appearance of Human Skin. Technical Report CUCS-024-05, Columbia University (2005)
Wai-Kin Kong, A., Zhang, D.: Competitive Coding Scheme for Palmprint Verification. In: Intl. Conf. on Pattern Recognition, vol. 1, pp. 520–523 (2004)
Sun, Z., Tan, T., Wang, Y., Li, S.Z.: Ordinal Palmprint Recognition for Personal Identification. Proc. of Computer Vision and Pattern Recognition (2005)
Lingyu, W., Leedham, G.: Near- and Far- Infrared Imaging for Vein Pattern Biometrics. In: Proc. of the IEEE Intl. Conf. on Video and Signal Based Surveillance (2006)
Tomasi, C., Manduchi, R.: Bilateral Filtering for Gray and Color Images. In: Proc. of Sixth Intl. Conf. on Computer Vision, pp. 839–846 (1998)
Anderson, R.R., Parrish, J.A.: The Science of Photomedicine. In: Optical Properties of Human Skin. ch. 6, Plenum Press, New York (1982)
Donoho, D.L., Duncan, M.R.: Digital Curvelet Transform: Strategy, Implementation and Experiments, available http://www-stat.stanford.edu/donoho/Reports/1999/DCvT.pdf
Candès, E.J., Donoho, D.L.: Curvelets – A Surprisingly Effective Nonadaptive Representation for Objects With Edges. In: Schumaker, L.L., et al. (eds.) Curves and Surfaces, Vanderbilt University Press, Nashville, TN (1999)
Curvelet website, http://www.curvelet.org/
Starck, J.L., Candès, E.J., Donoho, D.L.: The Curvelet Transform for Image Denoising. IEEE Transactions on Image Processing 11(6), 670–684 (2002)
Choi, M., Kim, R.Y., Nam, M.-R., Kim, H.O.: Fusion of Multispectral and Panchromatic Satellite Images Using the Curvelet Transform. IEEE Geoscience and Remote Sensing Letters 2(2) (2005)
Nencini, F., Garzelli, A., Baronti, S., Alparone, L.: Remote Sensing Image Fusion Using the Curvelet Transform. Information Fusion 8(2), 143–156 (2007)
Zhang, Q., Guo, B.: Fusion of Multisensor Images Based on Curvelet Transform. Journal of Optoelectronics Laser 17(9) (2006)
Zhang, Z., Blum, R.S.: A Categorization of Multiscale-Decomposition-Based Image Fusion Schemes with a Performance Study for a Digital Camera Application. Proc. of IEEE 87(8), 1315–1326 (1999)
Burt, P.J., Kolczynski, R.J.: Enhanced Image Capture Through Fusion. In: IEEE Intl. Conf. on Computer Vision, pp. 173–182. IEEE Computer Society Press, Los Alamitos (1993)
Sadjadi, F.: Comparative Image Fusion Analysais. IEEE Comptuer Vision and Pattern Recognition 3 (2005)
Petrović, V., Cootes, T.: Information Representation for Image Fusion Evaluation. In: Intl. Conf. on Information Fusion, pp. 1–7 (2006)
Petrović, V., Xydeas, C.: Objective Image Fusion Performance Characterisation. In: Intl. Conf. on Computer Vision, pp. 1868–1871 (2005)
Wang, Z., BovikA, A.C.: Univeral Image Quality Index. IEEE Signal Process Letter 9(3), 81–84 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hao, Y., Sun, Z., Tan, T. (2007). Comparative Studies on Multispectral Palm Image Fusion for Biometrics. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-76390-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
Online ISBN: 978-3-540-76390-1
eBook Packages: Computer ScienceComputer Science (R0)