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Detecting Eyes and Lips Using Neural Networks and SURF Features

Detecting Eyes and Lips Using Neural Networks and SURF Features

Artem A. Lenskiy, Jong-Soo Lee
Copyright: © 2012 |Pages: 17
ISBN13: 9781613504291|ISBN10: 1613504292|EISBN13: 9781613504307
DOI: 10.4018/978-1-61350-429-1.ch018
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MLA

Lenskiy, Artem A., and Jong-Soo Lee. "Detecting Eyes and Lips Using Neural Networks and SURF Features." Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, edited by Vijay Kumar Mago and Nitin Bhatia, IGI Global, 2012, pp. 338-354. https://doi.org/10.4018/978-1-61350-429-1.ch018

APA

Lenskiy, A. A. & Lee, J. (2012). Detecting Eyes and Lips Using Neural Networks and SURF Features. In V. Mago & N. Bhatia (Eds.), Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies (pp. 338-354). IGI Global. https://doi.org/10.4018/978-1-61350-429-1.ch018

Chicago

Lenskiy, Artem A., and Jong-Soo Lee. "Detecting Eyes and Lips Using Neural Networks and SURF Features." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, edited by Vijay Kumar Mago and Nitin Bhatia, 338-354. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-429-1.ch018

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Abstract

In this chapter, the authors elaborate on the facial image segmentation and the detection of eyes and lips using two neural networks. The first neural network is applied to segment skin-colors and the second to detect facial features. As for input vectors, for the second network the authors apply speed-up robust features (SURF) that are not subject to scale and brightness variations. The authors carried out the detection of eyes and lips on two well-known facial feature databases, Caltech. and PICS. Caltech gave a success rate of 92.4% and 92.2% for left and right eyes and 85% for lips, whereas the PCIS database gave 96.9% and 95.3% for left and right eyes and 97.3% for lips. Using videos captured in real environment, among all videos, the authors achieved an average detection rate of 94.7% for the right eye and 95.5% for the left eye with a 86.9% rate for the lips

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