Super-Resolution of Long Range Captured Iris Image Using Deep Convolutional Network
Deshpande Anand, Prashant P. Patavardhan · Advances in parallel computing · 2017
This chapter proposes a deep convolutional neural network based super-resolution framework to super-resolve and to recognize the long-range captured iris image sequences. The proposed framework is tested on CASIA V4 iris database by analyzing the peak signal-to-noise ratio (PSNR), structural similarity index matrix (SSIM) and visual information fidelity in pixel domain (VIFP) of the state-of-art algorithms. The performance of the proposed framework is analyzed for the upsampling factors 2 and 4 and achieved PSNRs of 37.42 dB and 34.74 dB respectively. Using this framework, we have achieved an equal error rate (EER) of 0.14%. The results demonstrate that the proposed framework can super-resolve the iris images effectively and achieves better recognition performance.