Iris Image Quality Parameters and Imaging Performance

The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enrol (FTE) rates. In this article, a signal to noise ratio (SNR), Grey scale Density, Contrast between sclera and Iris, contrast between iris and pupil quality measure for iris images is proposed. The merit of this approach lies in its ability to differentiate between the poor quality Images and the good quality images. An expression of quality based on utility reflects the predicted positive or negative contribution of an individual sample to the overall performance of biometric system. The term “quality” should not be solely attributable to the acquisition setting of the sample, such as image resolution, signal to noise ratio, grey scale density, contrast or numbers of parameters. Through such factors may affect sample utility and could contribute to overall quality score. Quality attributes impact authentic and imposter distributions. Effects on authentic and imposter distributions predict effects on match performance. Overall, this article suggests that it is possible to check and verify the quality of the iris image. It also provides several research directions for future work. Keywords— Signal to Noise Ratio, Grey Scale Density, Distortion, Pixel Aspect Ratio, Test Targets, Resolution