Realization of Human Eye Pupil Detection System using Canny Edge Detector and Circular Hough Transform Technique

Near Infrared (NIR) images involves the generation of an edge-map by combining two edge-maps generated from the same eye image for pupil detection. It is accomplished by the use of Gaussian filtering, picture binarization, and Sobel edge detection techniques. Image segmentation is used to group similar pixels based on the rate of change in intensity or depth, allowing for the representation of information from the image. The Hough transformation is employed as an efficient method for detecting lines in images, with this work proposing the use of angle-radius parameters instead of slope-intercept parameters, simplifying computation and facilitating pupil detection. This approach increases the accuracy and speed of pupil recognition by reducing erroneous edges in the edge-map. This technique's hardware implementation on an FPGA platform may be utilized for recognition and iris localization applications.