Detecting Eyes and Lips Using Neural Networks and SURF Features

View Sample PDF

Author(s): Artem A. Lenskiy (Korea University of Technology and Education, Korea)and Jong-Soo Lee (University of Ulsan, Korea)
Copyright: 2012
Pages: 17
Source title: Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies
Source Author(s)/Editor(s): Vijay Kumar Mago (Simon Fraser University, Canada)and Nitin Bhatia (DAV College, India)
DOI: 10.4018/978-1-61350-429-1.ch018

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

Purchase

View Detecting Eyes and Lips Using Neural Networks and SURF Features on the publisher's website for pricing and purchasing information.

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

The IRMA Community

Research IRM

Detecting Eyes and Lips Using Neural Networks and SURF Features

Purchase

Abstract

Related Content

IRMA Sponsors