Feature Characterization in Iris Recognition with Stochastic Autoregressive Models

Luis E. Garza Castañón; Saúl Montes de Oca; Rubén Morales-Menéndez

doi:10.1007/11874850_21

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006 pp 168-177

Feature Characterization in Iris Recognition with Stochastic Autoregressive Models

Authors
Authors and affiliations

Luis E. Garza Castañón
Saúl Montes de Oca
Rubén Morales-Menéndez

Conference paper

DOI: 10.1007/11874850_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)

Cite this paper as:: Castañón L.E.G., de Oca S.M., Morales-Menéndez R. (2006) Feature Characterization in Iris Recognition with Stochastic Autoregressive Models. In: Sichman J.S., Coelho H., Rezende S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. Lecture Notes in Computer Science, vol 4140. Springer, Berlin, Heidelberg

Abstract

Iris recognition is a reliable technique for identification of people. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. We are introducing stoch- astic autoregressive with exogenous inputs models for the features characterization step. Every model is learned from data. In the comparison and matching step, data taken from iris sample are substituted into every model and residuals are generated. A decision is taken based on a threshold calculated experimentally. A successful rate of identifications for UBIRIS and MILES databases shows potential applications.

Preview

Unable to display preview. Download preview PDF.

References

1.
Chen, C., Chu, C.: Low Complexity iris Recognition Based on Wavelet Probabilistic Neural Networks. In: Proc. of the Int. Joint Conf. on Neural Networks, vol. 3, pp. 1930–1935 (2005)Google Scholar
2.
Daugman, J.: How Iris Recognition Works. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
3.
Dobes, M., Machala, L., Tichasvky, P., Pospisil, J.: Human Eye Iris Recognition Using The Mutual Information. Optik (9), 399–404 (2004)Google Scholar
4.
Franklin, G., Powell, J., Workman, M.: Digital Control of Dynamic Systems, 3rd edn. Addison-Wesley, Reading (1997)Google Scholar
5.
Ganeshan, B., Theckedath, D., Young, R., Chatwin, C.: Biometric Iris Recognition System Using a Fast and Robust Iris Localization and Alignment Procedure. Optics and Lasers in Eng. 44, 1–24 (2006)CrossRefGoogle Scholar
6.
Garza Castañon, L.E., Montes de Oca, S., Morales-Menéndez, R.: An Application of Random and Hammersley Sampling Methods to Iris Recognition. In: To appear in the 19th Int. Conf. on Industrial, Engineering & Other Applications of Applied Intelligent SystemsOptics and Lasers in Eng., Annecy, Fr (June 2006)Google Scholar
7.
Huang, J., Wang, Y., Tan, T., Cui, J.: A New Iris Segmentation Method for Iris Recognition System. In: Proc. of the 17th Int Conf on Pattern Recognition, pp. 554–557 (2004)Google Scholar
8.
Independent Testing of Iris Recognition Technology Final Report, International Biometric Group (May 2005)Google Scholar
9.
Jain, A., Ross, A., Prabhakar, A.: An Introduction to Biometric Recognition. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 4–20 (2004)CrossRefGoogle Scholar
10.
Ma, L., Wang, Y., Tan, T., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)CrossRefGoogle Scholar
11.
Miles Research. Sample iris Pictures, http://www.milesresearch.com/
12.
Negin, M., Chmielewski, T., Salganicoff, M., Camus, T., Cahn, U., Venetianer, P., Zhang, G.: An Iris Biometric System for Public and Personal Use. Computer 33(2), 70–75 (2000)CrossRefGoogle Scholar
13.
Proenca, H., Alexandre, L.: UBIRIS: A Noisy Iris Image Database. In: Proc of the Int. Conf. on Image Analysis and Processing, vol. 1, pp. 970–977 (2005)Google Scholar

Copyright information

Authors and Affiliations

Luis E. Garza Castañón
- 1
Saúl Montes de Oca
- 2
Rubén Morales-Menéndez
- 1

1.Department of Mechatronics and AutomationITESM Monterrey Campus
2.Automation Graduate Program StudentITESM Monterrey CampusMonterreyMéxico

Personalised recommendations

Actions

Buy eBook

USD 119.00

Instant download
Readable on all devices
Own it forever
Local sales tax included if applicable

Feature Characterization in Iris Recognition with Stochastic Autoregressive Models

Abstract

Preview

Copyright information

Authors and Affiliations

Personalised recommendations

Cookies