Arbitrary illumination conditions for facial identification
Carlos M. Travieso, Jesús B. Alonso, Miguel A. Ferrer · 2007
This paper presents a simple, robust and novel for errors detection in biometric system which is applied to the Olivetti Research Laboratory (ORL) Face and Yale Databases. We have used as parameterisation different transformed dominions by James L. Wyman, et al. (2002), and a support vector machine (SVM) by Anil K. Jain, et al. (2004) as classifier. This system has been adjusted with our experiments for obtaining an false identification rate (FIR) of 0%, with a success rate of 90.8% a rejected samples rate of 9.2%.