Feature Characterization in Iris Recognition with Stochastic Autoregressive Models

  • 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luis E. Garza Castañón
    • 1
  • Saúl Montes de Oca
    • 2
  • Rubén Morales-Menéndez
    • 1
  1. 1.Department of Mechatronics and AutomationITESM Monterrey Campus 
  2. 2.Automation Graduate Program StudentITESM Monterrey CampusMonterreyMéxico

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