Active shape models for effective iris segmentation

Iris recognition has been demonstrated to be an efficient technology for doing personal identification. Performance of iris recognition system depends on the isolation of the iris region from rest of the eye image. In this work, effective use of active shape models (ASMs) for doing iris segmentation is demonstrated. A method for building flexible model by learning patterns of iris invariability from a well organized training set is described. The specific approach taken in the work sacrifices generality, in order to accommodate better iris segmentation. The algorithm was initially applied on the on-angle, noise free CASIA data base and then was extended to the off-axis iris images collected at WVU eye center. A direct comparison with canny iris segmentation in terms of error rates, demonstrate effectiveness of ASM segmentation. For the selected threshold value of 0.4, FAR and FRR values were 0.13% and 0.09% using canny detectors and 0% each using the proposed ASM based method.