Abstract
Palmprint biometric modality is one of the most challenging and active areas of research in the computer vision field due to its high accuracy, high user-friendliness, low cost, high level of security and usability. However, an automatic system designation is a challenging task due to the different variability produced in an unconstrained environment. Since the concept of fusion is robust against these variability, we propose in this paper a intra-modal palmprint fusion based on the features fusion extracted from left and right palms using the SIFT and HOG descriptors. This fusion scheme is performed at both the feature and score levels applying the Canonical Correlation Analysis and Geometric means metrics, respectively. Then, a Random Forest algorithm used in order generate the identity of a person. Practical experiments are performed over CASIA-Palmprint and IIT-Delhi datasets, report encouraging performances which are competitive to other well known palmprint identification approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abeysundera, H.P., Eskil, M.T.: Palmprint verification using sift majority voting. In: Computer and Information Sciences II, pp. 291–297. Springer, London (2011)
Arunkumar, M., Valarmathy, S.: Palm print identification using improved histogram of oriented lines. Circ. Syst. 7(08), 1665 (2016)
Charfi, N., Trichili, H., Alimi, A.M., Solaiman, B.: Local invariant representation for multi-instance toucheless palmprint identification. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 003522–003527. IEEE (2016)
Chen, J., Zhang, C., Rong, G.: Palmprint recognition using crease. In: Proceedings 2001 International Conference on Image Processing, vol. 3, pp. 234–237. IEEE (2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Volume 1, vol. 01, pp. 886–893. IEEE Computer Society (2005)
Duta, N., Jain, A.K., Mardia, K.V.: Matching of palmprints. Pattern Recogn. Lett. 23(4), 477–485 (2002)
Han, C.C., Cheng, H.L., Lin, C.L., Fan, K.C.: Personal authentication using palm-print features. Pattern Recogn. 36(2), 371–381 (2003)
Hong, D., Pan, Z., Wu, X.: Improved differential box counting with multi-scale and multi-direction: a new palmprint recognition method. Optik-Int. J. Light Electron Opt. 125(15), 4154–4160 (2014)
Jagadiswary, D., Saraswady, D.: Biometric authentication using fused multimodal biometric. Procedia Comput. Sci. 85, 109–116 (2016)
Kekre, H.B., Sarode, K., Tirodkar, A.A.: A study of the efficacy of using wavelet transforms for palm print recognition. In: 2012 International Conference on Computing, Communication and Applications (ICCCA), pp. 1–6. IEEE (2012)
Kumar, A., Zhang, D.: Personal authentication using multiple palmprint representation. Pattern Recogn. 38(10), 1695–1704 (2005)
Latha, Y.L.M., Prasad, M.V.N.K.: GLCM based texture features for palmprint identification system. In: Computational Intelligence in Data Mining, vol. 1, pp. 155–163. Springer, New Delhi (2015)
Leng, L., Li, M., Kim, C., Bi, X.: Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimedia Tools Appl. 76(1), 333–354 (2017)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer vision, 1999, vol. 2, pp. 1150–1157. IEEE (1999)
Michael, G.K.O., Connie, T., Teoh, A.B.J.: A contactless biometric system using multiple hand features. J. Vis. Commun. Image Representat. 23(7), 1068–1084 (2012)
Mokni, R., Drira, H., Kherallah, M.: Fusing multi-techniques based on LDA-CCA and their application in palmprint identification system. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 350–357. IEEE (2017)
Mokni, R., Drira, H., Kherallah, M.: Multiset canonical correlation analysis: texture feature level fusion of multiple descriptors for intra-modal palmprint biometric recognition. In: Pacific-Rim Symposium on Image and Video Technology, pp. 3–16. Springer, Cham (2017)
Mokni, R., Elleuch, M., Kherallah, M.: Biometric palmprint identification via efficient texture features fusion. In: International Joint Conference on Neural Networks, pp. 4857–4864 (2016)
Mokni, R., Zouari, R., Kherallah, M.: Pre-processing and extraction of the ROIs steps for palmprints recognition system. In: International Conference on Intelligent Systems Design and Applications, pp. 380–385 (2015)
Raghavendra, R., Busch, C.: Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition. Pattern Recogn. 47(6), 2205–2221 (2014)
Sun, Q.S., Zeng, S.G., Liu, Y., Heng, P.A., Xia, D.S.: A new method of feature fusion and its application in image recognition. Pattern Recogn. 38(12), 2437–2448 (2005)
Xu, Y., Fei, L., Zhang, D.: Combining left and right palmprint images for more accurate personal identification. IEEE Trans. Image Process. 24(2), 549–559 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mokni, R., Kherallah, M. (2020). Efficient Personal Identification Intra-modal System by Fusing Left and Right Palms. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_70
Download citation
DOI: https://doi.org/10.1007/978-3-030-16660-1_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16659-5
Online ISBN: 978-3-030-16660-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)