Abstract
Hand biometrics relies strongly on a proper hand segmentation and a feature extraction method to obtain accurate results in individual identification. Former operations must be carried out involving as less user collaboration as possible, in order to avoid intrusive or invasive actions on individuals.
This document presents an approach for hand segmentation and feature extraction on scenarios where users can place the hand on a flat surface freely, without no constraint on hand openness, rotation and pressure.
The performance of the algorithm highlights the fact that in less than 4 seconds, the method can detect properly finger tips and valleys with a global accuracy of 97% on a database of 300 users, achieving the second position in the International Hand Geometric Competition HGC 2011.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Alhussain, T., Drew, S., Alfarraj, O.: Biometric authentication for mobile government security. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS), vol. 2, pp. 114–118 (2010) iD: 1
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (June 2007)
Arif, M., Brouard, T., Vincent, N.: Personal identification and verification by hand recognition. In: 2006 IEEE International Conference on Engineering of Intelligent Systems, pp. 1–6 (2006)
Ashbourn, J.: Practical implementation of biometrics based on hand geometry. In: IEE Colloquium on Image Processing for Biometric Measurement, pp. 5/1–5/6 (1994) iD: 1
de Santos Sierra, A., Guerra Casanova, J., Sánchez Ávila, C., Jara Vera, V.: Silhouette-based hand recognition on mobile devices. In: 43rd Annual 2009 International Carnahan Conference on Security Technology, pp. 160–166 (October 2009)
Doublet, J., Lepetit, O., Revenu, M.: Contact less hand recognition using shape and texture features. In: 2006 8th International Conference on Signal Processing, vol. 3 (2006) iD: 1
Doublet, J., Lepetit, O., Revenu, M.J.: Contactless hand recognition based on distribution estimation. In: Biometrics Symposium, pp. 1–6 (2007) iD:1
Ferrer, M., Fabregas, J., Faundez, M., Alonso, J., Travieso, C.: Hand geometry identification system performance. In: 43rd Annual 2009 International Carnahan Conference on Security Technology, 2009, pp. 167–171 (5-8, 2009)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Longman Publishing Co., Inc., Boston (1992)
Kanhangad, V., Kumar, A., Zhang, D.: Combining 2d and 3d hand geometry features for biometric verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 39–44 (20-25, 2009)
Lew, Y.P., Ramli, A.R., Koay, S.Y., Ali, R., Prakash, V.: A hand segmentation scheme using clustering technique in homogeneous background. In: Student Conference on Research and Development, SCOReD 2002, pp. 305–308 (2002) iD: 1
Magalhaes, F., Oliveira, H.P., Matos, H., Campilho, A.: hGC2011 - Hand Geometric Points Detection Competition Database (2010), http://www.fe.up.pt/~hgc2011/
Morales, A., Ferrer, M., Alonso, J., Travieso, C.: Comparing infrared and visible illumination for contactless hand based biometric scheme. In: 42nd Annual IEEE International Carnahan Conference on Security Technology, ICCST 2008, pp. 191–197 (2008)
García-Casarrubios Muñoz, Á., Sánchez Ávila, C., de Santos Sierra, A., Guerra Casanova, J.: A Mobile-Oriented Hand Segmentation Algorithm Based on Fuzzy Multiscale Aggregation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010, Part I. LNCS, vol. 6453, pp. 479–488. Springer, Heidelberg (2010)
Munoz, A.C., de Santos Sierra, A., Ávila, C., Casanova, J., del Pozo, G., Vera, V.: Hand biometric segmentation by means of fuzzy multiscale aggregation for mobile devices. In: 2010 International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), pp. 1–6 (2010)
Sanchez-Reillo, R., Sanchez-Avila, C., Gonzalez-Marcos, A.: Biometric identification through hand geometry measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1168–1171 (2000)
Spruyt, V., Ledda, A., Geerts, S.: Real-time multi-colourspace hand segmentation. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3117–3120 (2010) iD: 1
Yoruk, E., Konukoglu, E., Sankur, B., Darbon, J.: Shape-based hand recognition. IEEE Transactions on Image Processing 15(7), 1803–1815 (2006)
Zheng, G., Wang, C.J., Boult, T.E.: Application of projective invariants in hand geometry biometrics. IEEE Transactions on Information Forensics and Security 2(4), 758–768 (2007) iD: 1
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Santos Sierra, A., Sánchez Ávila, C., Guerra Casanova, J., del Pozo, G.B. (2011). Invariant Hand Biometrics Feature Extraction. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-25449-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25448-2
Online ISBN: 978-3-642-25449-9
eBook Packages: Computer ScienceComputer Science (R0)