Abstract
Existing ear recognition approaches do not give theoretical or experimental performance prediction. Therefore, the discriminating power of ear biometric for human identification cannot be evaluated. This paper addresses two interrelated problems: (a) proposes an integrated local descriptor for representation to recognize human ears in 3D. Comparing local surface descriptors between a test and a model image, an initial correspondence of local surface patches is established and then filtered using simple geometric constraints. The performance of the proposed ear recognition system is evaluated on a real range image database of 52 subjects. (b) A binomial model is also presented to predict the ear recognition performance. Match and non-matched distances obtained from the database of 52 subjects are used to estimate the distributions. By modeling cumulative match characteristic (CMC) curve as a binomial distribution, the ear recognition performance can be predicted on a larger gallery.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Iannarelli, A.: Ear Identification. Forensic Identification Series. Paramont Publishing Company (1989)
Burge, M., Burger, W.: Ear biometrics in computer vision. In: Proc. Int. Conf. on Pattern Recognition, vol. 2, pp. 822–826 (2000)
Hurley, D., Nixon, M., Carter, J.: Automatic ear recognition by force field transformations. IEE Colloquium on Visual Biometrics, 7/1 –7/5 (2000)
Chang, K.C., Bowyer, K.W., Sarkar, S., Victor, B.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. Pattern Analysis and Machine Intelligence 25, 1160–1165 (2003)
Bhanu, B., Chen, H.: Human ear recognition in 3D. In: Workshop on Multimodal User Authentication, pp. 91–98 (2003)
Bronstein, A., Bronstein, M., Kimmel, R.: Expression-invariant 3D face recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 62–70. Springer, Heidelberg (2003)
Chang, K.C., Bowyer, K.W., Flynn, P.J.: Multi-modal 2D and 3D biometrics for face recognition. In: IEEE Int. Workshop on Analysis and Modeling of Faces and Gestures, pp. 187–194 (2003)
Chua, C.S., Han, F., Ho, Y.: 3D human face recognition using point signatures. In: Int. Conf. on Automatic Face and Gesture Recognition, pp. 233–238 (2000)
Lee, J.C., Milios, E.: Matching range images of human faces. In: Proc. Int. Conf. on Computer Vision, pp. 722–726 (1990)
Lu, X., Colbry, D., Jain, A.K.: Three-dimensional model based face recognition. In: Proc. Int. Conf. on Pattern Recognition, vol. 1, pp. 362–366 (2004)
Bhanu, B., Wang, R., Tan, X.: Predicting fingerprint recognition performance from a small gallery. In: ICPR Workshop on Biometrics: Challenges arising from Theory to Practice, pp. 47–50 (2004)
Tan, X., Bhanu, B.: On the fundamental performance for fingerprint matching. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 499–504 (2003)
Johnson, A.Y., Sun, J., Boick, A.F.: Predicting large population data cumulative match characteristic performance from small population data. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 821–829. Springer, Heidelberg (2003)
Wayman, J.L.: Error-rate equations for the general biometric system. IEEE Robotics & Automation Magazine 6, 35–48 (1999)
Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recognition 36, 279–291 (2003)
Johnson, A.Y., Sun, J., Boick, A.F.: Using similarity scores from a small gallery to estimate recognition performance for large galleries. In: IEEE Int. Workshop on Analysis and Modeling of Faces and Gestures, pp. 100–103 (2003)
Grother, P., Phillips, P.J.: Models of large population recognition performance. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 68–75 (2004)
Besl, P., Mckay, N.D.: A method of registration of 3-D shapes. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Flynn, P., Jain, A.: On reliable curvature estimation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 110–116 (1989)
Dorai, C., Jain, A.: COSMOS-A representation scheme for free-form surfaces. In: Proc. Int. Conf. on Computer Vision, pp. 1024–1029 (1995)
Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Analysis and Machine Intelligence 21, 433–449 (1999)
Koenderink, J.J., Doorn, A.V.: Surface shape and curvature scales. Image Vision Computing 10, 557–565 (1992)
Schiele, B., Crowley, J.: Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision 36, 31–50 (2000)
Horn, B.: Close-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America 4, 629–642 (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, H., Bhanu, B., Wang, R. (2005). Performance Evaluation and Prediction for 3D Ear Recognition. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_78
Download citation
DOI: https://doi.org/10.1007/11527923_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
eBook Packages: Computer ScienceComputer Science (R0)