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Proceedings Paper

Eye feature points detection by CNN with strict geometric constraint
Author(s): Chunning Meng; Xuepeng Zhao; Mingkui Feng; Shengjiang Chang
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Paper Abstract

The detection accuracy of facial landmarks or eye feature points is influenced by geometric constraint between the points. However, this constraint is far from being research in existing convolutional neural network (CNN) based points detection. Whether strict geometric constraint can improve the performance is not studied yet. In this paper, we propose a new approach to estimate the eye feature points by using single CNN. A deep network containing three convolutional layers is built for points detection. To analyze the influence of geometric constraint on CNN based points detection, three definitions of the eye feature points are proposed and used for calibration. The experiments show that excellent performance is achieved by our method, which prove the importance of the strict geometric constraint in points detection based on CNN. In addition, the proposed method achieves high accuracy of 96.0% at 5% detection error, but need less computing time than the cascade structure.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003340 (29 August 2016); doi: 10.1117/12.2245167
Show Author Affiliations
Chunning Meng, China Maritime Police Academy (China)
Xuepeng Zhao, Nankai Univ. (China)
Mingkui Feng, China Maritime Police Academy (China)
Shengjiang Chang, Nankai Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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