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

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Computer Vision

Background:

• Smartphone image quality now rivals medical imaging.
• Accurate pupil diameter measurement is crucial for various applications.
• Existing methods may struggle in diverse lighting conditions.

Purpose of the Study:

• To develop and validate novel smartphone-based algorithms for pupil recognition.
• To measure pupil diameter accurately in both indoor and outdoor environments.
• To compare the proposed algorithms against traditional measurement methods.

Main Methods:

• Developed a stateful-service-based pupil recognition mechanism (PRSSM) for indoor lighting.
• Developed a color component low-pass filtering (CCLPF) algorithm for outdoor sunlight.
• Utilized RGB to HSV conversion, adaptive thresholding, morphological operations, and contour detection.
• Employed ruler-based measurements for accuracy verification and comparison.

Main Results:

• PRSSM algorithm accurately determined pupil diameter in indoor natural light.
• CCLPF algorithm accurately determined pupil diameter in outdoor sunlight.
• Both PRSSM and CCLPF algorithms demonstrated smaller errors than a comparative algorithm.
• The algorithms proved effective for pupil diameter analysis under varied lighting.

Conclusions:

• Smartphone-based pupil recognition is feasible and accurate.
• The PRSSM and CCLPF algorithms provide reliable pupil diameter measurements.
• These algorithms offer a promising, accessible tool for ophthalmic research and diagnostics.