Stateful-Service-Based Pupil Recognition in Natural Light Environments.

Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.