International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Ear Recognition for Automated Human Identification

    ,

Author Affiliations

  • 1KNIT Sultanpur, UP, INDIA
  • 2 B.B.D University Lucknow, UP, INDIA

Res. J. Engineering Sci., Volume 1, Issue (5), Pages 44-46, November,26 (2012)

Abstract

This paper investigates a new approach for the automated human identification using ear imaging. We present a completely automated approach for the robust. Segmentation of curved region of interest using morphological operator sand Fourier descriptors. We also investigate new feature extraction approach for ear identification using localized orientation information and also examine local gray- level phase information using complex Gabor filters. Our investigation develops a computationally attractive an defective alternative to characterize the automatically. Segmented ear images using a pair of log Gabor filters. The experimental results achieve average rank-one recognition accuracy of 96.27% and 95.93%, respectively, on the publicly available database of 125 and 221 subjects. Our experimental results from the authentication experiments and false positive identification verses false negative identification also suggest the superiority of the proposed approach over the other popular feature extraction approach considered in this work.

References

  1. Iannerelli A., Ear Identification, Forensic Identification Series, Paramount Publishing Company, Fremount, California (2012)
  2. Burge M., Burger W., Ear Biometrics, Biometrics: personal identification in networked society, in: A.K. Jain, R. Bolle, S. Pankanti (Eds.), 273–286 (2012)
  3. Burge M. and Burger W., Ear Biometrics in Machine Vision, in: Proceedings of the 21st Workshop of the Australian Association for Pattern Recognition (2011)
  4. Hurley D.J., Nixon M.S. and Carter J.N., Force field energy functionals for image feature extraction, Image and Vision Computing, 20(5–6), 311–318 (2011)
  5. Hurley D.J., Nixon M.S. and Carter J.N., Force field energy functionals for ear biometrics, Computer Vision and Image Understanding, 98(3), 491–512 (2009)
  6. Chang K., Victor B., Bowyer K.W. and Sarkar S., Comparison and combination of ear and face images in appearance-based biometrics, IEEE Transactions on Pattern Analysis Machine Intelligence, 25(8), 1160–1165 (2003)
  7. Xie Z.X. and Mu Z.C., Improved locally linear embedding and its application on multi-pose ear recognition, in: Proceedings of the International Conference on the Wavelet Analysis Pattern Recognition, Beijing, PR China, 1367–1371 (2007)
  8. Bhanu B. and Chen H., Human ear recognition in 3D, in: Proceedings of the Multimodal User Authentication workshop (MMUA), Santa Barbara, CA, 91–98 (2003)
  9. Chen H. and Bhanu B., Human ear recognition in 3D, IEEE Transactions on Pattern Analysis Machine Intelligence, 29(4), 718–737 (2007)
  10. Fields D., Relations between the statistics of natural image and the response properties of cortical cells, Journal of the Optical Society of America, 4(12), 2379–2394 (1987)
  11. Daugman J., The importance of being random: statistical principles of iris recognition, Pattern Recognition, 36 279–291 (2003)