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http://dx.doi.org/10.14400/JDC.2016.14.10.271

Performance Improvement for Robust Eye Detection Algorithm under Environmental Changes  

Ha, Jin-gwan (Department of Computer Science and Engineering, Sejong University)
Moon, Hyeon-joon (Department of Computer Science and Engineering, Sejong University)
Publication Information
Journal of Digital Convergence / v.14, no.10, 2016 , pp. 271-276 More about this Journal
Abstract
In this paper, we propose robust face and eye detection algorithm under changing environmental condition such as lighting and pose variations. Generally, the eye detection process is performed followed by face detection and variations in pose and lighting affects the detection performance. Therefore, we have explored face detection based on Modified Census Transform algorithm. The eye has dominant features in face area and is sensitive to lighting condition and eye glasses, etc. To address these issues, we propose a robust eye detection method based on Gabor transformation and Features from Accelerated Segment Test algorithms. Proposed algorithm presents 27.4ms in detection speed with 98.4% correct detection rate, and 36.3ms face detection speed with 96.4% correct detection rate for eye detection performance.
Keywords
Modified Census Transform; Gabor Transform; Features from Accelerated Segment Test; Face Detection; Eye Detection; Pupil Detection;
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