ScholarWorks@동국대학교

초록

The classification of eye openness and closure has been researched in various fields, e.g., driver drowsiness detection, physiological status analysis, and eye fatigue measurement. For a classification with high accuracy, accurate segmentation of the eye region is required. Most previous research used the segmentation method by image binarization on the basis that the eyeball is darker than skin, but the performance of this approach is frequently affected by thick eyelashes or shadows around the eye. Thus, we propose a fuzzy-based method for classifying eye openness and closure. First, the proposed method uses I and K color information from the HSI and CMYK color spaces, respectively, for eye segmentation. Second, the eye region is binarized using the fuzzy logic system based on I and K inputs, which is less affected by eyelashes and shadows around the eye. The combined image of I and K pixels is obtained through the fuzzy logic system. Third, in order to reflect the effect by all the inference values on calculating the output score of the fuzzy system, we use the revised weighted average method, where all the rectangular regions by all the inference values are considered for calculating the output score. Fourth, the classification of eye openness or closure is successfully made by the proposed fuzzy-based method with eye images of low resolution which are captured in the environment of people watching TV at a distance. By using the fuzzy logic system, our method does not require the additional procedure of training irrespective of the chosen database. Experimental results with two databases of eye images show that our method is superior to previous approaches. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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