We describe and analyze the performance of a non-ideal iris recognition system. The system is designed to process non-ideal iris images in two steps: (i) estimation of the gaze direction and (ii) processing and encoding of the rotated iris image. We use two objective functions to estimate the gaze direction: Hamming distance and Daugman's integro-differential operator and determine an estimated angle by picking the value that optimizes the selected objective function. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed in this work is based on application of the global independent component analysis (ICA) to masked iris images. We use two datasets: CASIA dataset and a special dataset of off-angle iris images collected at WVU to verify the performance of the encoding technique and angle estimator, respectively. A series of receiver operating characteristics (ROCs) demonstrates various effects on the performance of the non-ideal iris based recognition system implementing the global ICA encoding.
[1]
Ashok A. Ghatol,et al.
Iris recognition: an emerging biometric technology
,
2007
.
[2]
Libor Masek,et al.
Recognition of Human Iris Patterns for Biometric Identification
,
2003
.
[3]
John Daugman,et al.
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
,
1993,
IEEE Trans. Pattern Anal. Mach. Intell..
[4]
Dexin Zhang,et al.
Personal Identification Based on Iris Texture Analysis
,
2003,
IEEE Trans. Pattern Anal. Mach. Intell..
[5]
Stephanie Schuckers,et al.
Biorthogonal-wavelets-based iris recognition
,
2005,
SPIE Defense + Commercial Sensing.
[6]
D. M. Etter,et al.
Analysis of partial iris recognition
,
2005,
SPIE Defense + Commercial Sensing.
[7]
S. Noh,et al.
MULTIRESOLUTION INDEPENDENT COMPONENT ANALYSIS FOR IRIS IDENTIFICATION
,
2002
.
[8]
Luca Bogoni,et al.
Iris Recognition at a Distance
,
2005,
AVBPA.