Analyzing human pupillary behavior is a noninvasive and alternative method for assessing neurological activity. Changes in this behavior are correlated with various health conditions, such as Parkinson’s, Alzheimer’s, autism and diabetes. Examining pupil behavior is a simple, low-cost method that can be used as a complementary diagnosis in comparison with other neurological evaluation methods. This approach is made by recording the pupillary behavior against light stimuli and measuring the pupil diameter through the video. The relation of pupillometry with digital image processing creates a dependency for computer vision based systems. Therefore, this paper presents a systematic review of the literature (SRL) conducted in order to analyze the progress of pupillometry systems based on computer vision. The main goal was to establish the state of art and identify possible gaps.