RE-PUPIL: resource efficient pupil detection system using the technique of average black pixel density

The pupil detection algorithm plays a key role in the non-contact tono-meter, auto ref-keratometry and optical coherence tomography in medical ophthalmology diagnostic equipment. A major challenge associated with pupil detection techniques is the use of conventional neural networks based on algorithms, integro-differential operator and circular hough transform, which leads to inefficient use of hardware resources in FPGA. To overcome this, using an average black pixel density technique, the proposed human eye pupil detection system is used to easily recognize and diagnose the human eye pupil area. Double threshold, logical OR, morphological closing and average black pixel density modules are involved in the proposed solution. To test the proposed method, the near infrared (NIR) iris databases are being used, namely: CASIA-IrisV4 and IIT Delhi and have achieved 98% percent accuracy, specificity, sensitivity. The proposed work was synthesized via Zynq XC7Z020 FPGA and the results are compared with previous approaches.