<p>Pupil assessment considered an important stage in the clinical examination to detect wide range of eye conditions, from simple ocular diseases to complex neurological disorders. The physical properties of the human pupil like shape and size can be used as a good indicator of human’s overall health. In the case of eye disease, such as glaucoma, different changes to the shape of the human eye may occurred and hence its ability to move may be infected. For pupil detection, the color of the human skin influences on the manual thresholding and ultimately the need for an automatic thresholding technique is a critical need. The current study used an automatic technique to detect the human pupil through the use of the Iris V3 dataset from the Chinese Academy of the Scientific Research Institute of Automation (CASIA). The technique utilized a combination of threshold and connected component analyses through assuming that the largest connected component within a certain range is the pupil under search. The originality of the current study lies in its flexibility in pupil detection visually with automatic procedure to select the optimal threshold thereby allowing dynamic interaction and the possibility for the user to control, adjust the parameters, and display the results interactively. A calculation of the pupil center and distance are performed and the results revealed a state-of-art performance with an accurate and precise pupil detection.</p>