Extracting gender discriminating features from facial needle-maps

Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Smith, W. A. P, Hancock, E. R. and Kawulok, Michal 2009. Extracting gender discriminating features from facial needle-maps. Presented at: 2009 IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, 7-10 Nov 2009. 2009 IEEE International Conference on Image Processing: Proceedings. Piscataway, NJ: IEEE, pp. 2449-2452. 10.1109/ICIP.2009.5414129

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Abstract

In this paper, we show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features. The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature. Experimental results illustrate the effectiveness of this method and its successful application to gender classification.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	9781424456536
Related URLs:	Publisher Organisation
Last Modified:	01 Nov 2022 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/89865

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