IRO Journal on Sustainable Wireless Systems
IRO Journals
Volume 6 — Issue 4 — December 2024

Oculang: Empowering Communication through Blink Language Detection

https://doi.org/10.36548/jsws.2024.4.003
Suriya S.
Associate Professor,Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Anubharathi V S.
UG scholars,Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Bharathi G.
UG scholars,Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Harshada R.
UG scholars,Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Vignesh Aditya R.
UG scholars,Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Sundarsree B G.
UG scholars,Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
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How to Cite

S., Suriya, Anubharathi V S., Bharathi G., Harshada R., Vignesh Aditya R., and Sundarsree B G. 2025. “Oculang: Empowering Communication through Blink Language Detection”. IRO Journal on Sustainable Wireless Systems 6 (4): 318-32. https://doi.org/10.36548/jsws.2024.4.003.
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Keywords

— Computer Vision
— Human-Computer Interaction
— Assistive Technology
— Accessibility
— Vision-Based Interaction
— Facial Landmark Detection
— Gaze Estimation
— Thresholding Techniques
— Geometric Ratios
Published: 22-01-2025

Abstract

Oculang is a communication system that uses computer vision and machine learning techniques to enable individuals with neurodegenerative disorders to communicate using eye gestures. The application focuses on detecting facial landmarks, gaze estimation, and blink detection to analyze combinations of eye movements captured from video input and produce message outputs. Dlib’s shape predictor (68 facial landmarks) and OpenCV-based image processing methods are used to extract and process the features of the eye region. A decision-making algorithm maps the detected gestures and predefined keywords for message generation. The application supports both real-time video capturing and uploading video through a Django-based user interface. Experimental evaluation on recorded datasets of eye movements demonstrated robust performance in accurately recognizing blinks, winks, and gaze directions, validating the system’s reliability.

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