A peer-reviewed, free-access online, free publication with impact factor
ISSN: 1686-9869 (Print), ISSN: 2697-5548 (Online)
วารสารวิชาการเทคโนโลยีอุตสาหกรรม (J. Ind. Tech.) อยู่ในฐานข้อมูล Thai-Journal Citation Index Centre (TCI) กลุ่ม 1 (2564 - 2567) และ Asean Citation Index (ACI) มีค่า JIF = 0.094 และ T-JIF (3 ปีย้อนหลัง): 0.165 | The Journal of Industrial Technology (J. Ind. Tech.) is indexed in Thai-Journal Citation Index Centre (TCI) Tier 1 (2021 - 2024) and Asean Citation Index (ACI) with impact factor, T-JIF: 0.094 and 3-years T-JIF: 0.165
บทความวิชาการ >>Iris Region and Bayes Classifier for Robust Open or Closed Eye Detection
Iris Region and Bayes Classifier for Robust Open or Closed Eye Detection
This paper presents a robust method to detect sequence of state open or closed of eye in low-resolution
image which can finally lead to efficient eye blink detection for practical use. Eye states and eye blink detection
play an important role in human-computer interaction (HCI) systems. Eye blinks can be used as communication
method for people with severe disability providing an alternate input modality to control a computer or as detection
method for a driver s drowsiness. The proposed approach is based on an analysis of eye and skin in eye region
image. Evidently, the iris and sclera regions increase as a person opens an eye and decrease while an eye is closing.
In particular, the distributions of these eye components, during each eye state, form a bell-like shape. By using color
tone differences, the iris and sclera regions can be extracted from the skin. Next, a naive Bayes classifier effectively
classifies the eye states. Further, a study also shows that iris region as a feature gives better detection rate over
sclera region as a feature. The approach works online with low-resolution image and in typical lighting conditions.
It was successfully tested in 9 image sequences (2,210 frames) and achieved high accuracy of over 92% for open
eye and over 86% for closed eye compared to the ground truth. In particular, it improves almost 15% in terms of
open eye state detection compared to a recent commonly used approach, template matching algorithm.