Abstract
Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.
Highlights
IntroductionBiometric iris recognition has been extensively investigated for accurate and automatic personal identification and authentication [2]
The stability of iris patterns over the human lifespan and their uniqueness was first noticed in 1987 [1].Since biometric iris recognition has been extensively investigated for accurate and automatic personal identification and authentication [2]
We presented a fast image quality metric, based on statistical features of the sign–magnitude transform to estimate the quality of iris images acquired by handheld devices in visible light
Summary
Biometric iris recognition has been extensively investigated for accurate and automatic personal identification and authentication [2]. Most commercial iris recognition systems use near-infrared (NIR). Due to the popularity of smartphones and similar handheld devices with digital cameras, iris recognition systems using images taken in visible light have recently been developed [3,4,5]. Image quality is a key factor affecting the performance of iris recognition systems [6,7,8]. In the biometric recognition literature, a biometric quality measure is a covariate that is measurable, influences performance, and is actionable [9,10,11]. Quality measurement can include subject and image covariates
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