Multimodal Biometric Authentication System Using Local Hand Features

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

In this work, the hand-based multimodal biometric system is presented using score-level fusion of hand geometry and local palmprint features. Initially, a palm ROI of fixed size has been cropped on the basis of finger base points. However, these images are not well aligned and reduce the matching accuracy. To better align them, L-K tracking-based palm image alignment method has been presented. Following this, the poor contrast ROI image is enhanced using novel fractional G-L filter. Then, local keypoints of aligned ROI images are extracted using Block–SIFT descriptor. Secondly, a set of novel geometrical features has been computed from Palmer region of hand image. Further, the highly uncorrelated features are selected from palm and hand geometry using Dia-FLD. In order to handle robust classification, a high-performance method Linear SVM has been used. Finally, score-level fusion rule has been employed which has shown the increased performance of combined approach in terms of Correct Recognition Rate (99.34%), Equal Error Rate (2.16%), and Computation Time (2084 ms). The proposed system has been tested on largest publicly available contact based and contactless databases: Bosphorus hand database, CASIA, and IITD palmprint databases to validate the results.

Keywords

Fusion Local features SIFT Lucas–Kanade tracking 

References

  1. 1.
    Jaswal, G., Kaul, A., Nath, R.: Knuckle print biometrics and fusion schemes-overview, challenges, and solutions. ACM Comput. Surv. 49(2), 34 (2016)CrossRefGoogle Scholar
  2. 2.
    Wong, K.Y., Chekima, A., Dargham, J.A., Sainarayanan, G.: Palmprint identification using Sobel operator. In: 10th IEEE International Conference on Control, Automation, Robotics and Vision, pp. 1338–1341 (2008)Google Scholar
  3. 3.
    Zhang, L.W., Zhang, B., Yan, J.: Principal line-based alignment refinement for palmprint recognition. IEEE Trans. Syst. Man Cybern. 42(6): 1491–1499 (2012)Google Scholar
  4. 4.
    Wu, X., Zhao, Q., Bu, W.: A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors. Pattern Recogn. 47(10): 3314–3326 (2014)CrossRefGoogle Scholar
  5. 5.
    Wu, X., Zhang, D., Wang, K.: Fisherpalms based palmprint recognition. Pattern Recogn. Lett. 24(15): 2829–2838 (2003)CrossRefGoogle Scholar
  6. 6.
    Kong, A.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: 17th IEEE International Conference on Pattern Recognition, pp. 520–523 (2004)Google Scholar
  7. 7.
    Sanchez-Reillo, R.: Hand geometry pattern recognition through Gaussian mixture modelling. In: 15th IEEE International Conference on Pattern Recognition, Vol. 2, pp. 937–940 (2000)Google Scholar
  8. 8.
    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, pp. 39–44 (2009)Google Scholar
  9. 9.
    Sharma, S., Dubey, S.R., Singh, S.K., Saxena, R., Singh, R.K.: Identity verification using shape and geometry of human hands. Expert Syst. Appl. 42(2), 821–832 (2015)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    CASIA palmprint Database. http://biometrics.idealtest.org/
  12. 12.
  13. 13.
    Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vision 56(3), 221–255 (2004)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Noushath, S., Kumar, G., Kumara, P.: Diagonal fisher linear discriminant analysis for efficient face recognition. Neurocomputing 69(13), 1711–1716 (2006)CrossRefGoogle Scholar
  16. 16.
    Michael, G.K.O., Connie, T., Teoh, A.B.J.: A contact-less biometric system using multiple hand features. J. Vis. Commun. Image Represent. 23(7), 1068–1084 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Signal Processing and Instrumentation Laboratory, Department of Electrical EngineeringNational Institute of TechnologyHamirpurIndia

Personalised recommendations