Using quantitative evaluation, we show that constrained least-square (CLS)-based image restoration extends depth of capture volume (DCV) significantly, and suggest that performance degradation characteristics of a feature extractor by defocus blurring should be considered while developing unconstrained iris recognition systems. When developing an unconstrained iris recognition system (assuming a relatively cooperative user) the extension of the DCV clearly stands out as one of the most important problems. Although it has already been reported that CSL-based image restoration can improve recognition performance by mitigating the effect of defocus blurring, until now there have been no reports as to how much the DCV could be extended. This work inspects the Hamming distance (HD) distribution and error rate while changing the strength of defocus blurring with iris images of CASIA-IrisV3-Interval. It is derived that the ratio of the maximum blurring parameters satisfying a certain acceptable error rate is the ratio of the DCV. Using this fact, experimental results show that CLS-based image restoration extends the DCV by more than 70%. Additionally, it is observed that a log-Gabor filter is superior to a Gabor filter as the feature extractor. It should be noted that although these two feature extractors show a relatively small performance gap when a focused image set is used, the gap becomes significantly larger as the blurring becomes more severe. This is thought to suggest an important principle that performance characteristics should be considered with respect to defocus blurring when selecting or developing the feature extractor of an unconstrained iris recognition system.
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics