3D facial image recognition using a nose volume and curvature based Eigenface

Lee, Yeunghak, Kim, Ikdong, Shim, Jaechang and Marshall, Andrew David ORCID: https://orcid.org/0000-0003-2789-1395 2006. 3D facial image recognition using a nose volume and curvature based Eigenface. Lecture Notes in Computer Science 4077 , pp. 616-622. 10.1007/11802914_48

Full text not available from this repository.

Abstract

The depth information in the face represents personal features in detail. In this study, the important personal facial information was presented by the surface curvatures and the features of vertical and horizontal of nose volume extracted from the face. The approach works by the depth of nose, the area of nose and the volume of nose based both on a vertical and horizontal are calculated. And the principal components analysis (PCA), which is calculated using the curvature data, was presented different features for each person. To classify the faces, the cascade architectures of fuzzy neural networks (CAFNNs), which can guarantee a high recognition rate as well as parsimonious knowledge base, are considered. In the experimental results, 3D images demonstrate the effectiveness of the proposed methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISSN:	0302-9743
Last Modified:	21 Oct 2022 10:56
URI:	https://orca.cardiff.ac.uk/id/eprint/41784

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item