3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2433 |
| Date | January 2008 |
| Creators | Qatawneh, S., Ipson, Stanley S., Qahwaji, Rami S.R., Ugail, Hassan |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, Accepted Manuscript |
| Rights | © 2008 IASTED and ACTA Press. Reproduced in accordance with the publisher's self-archiving policy |
| Relation | http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=494#pages |
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