Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Engineering and Mathematics
RAS ID
2044
Abstract
This paper describes a new approach to face detection. A colour input image is first processed using neural networks to detect skin regions in the image. Each neural network separates skin and non-skin pixels on the basis of chrominance information. The skin-colour classifier employs the committee machine technique, which improves skin colour detection by combining the classification results of a set of multilayer perceptrons (MLPs). The skin colour classifier achieves a classification rate of 84% compared to 81% for the best individual MLP classifier. The output of the committee machine is processed by a 2D smoothing filter before being converted into a binary map using a threshold. Finally, several post-processing techniques based on shape and luminance features are proposed for rejecting non-facial regions
DOI
10.1109/ANZIIS.2001.974071
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: Phung, S. L., Chai, D. K., & Bouzerdoum, A. (2001). Skin colour based face detection. Proceedings of 7th Australian and New Zealand Intelligent Information Systems Conference. (pp. 0). Australia. IEEE. Available here
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