2D-3D Face Recognition Based on PCA and Feature Modelling


Autoria(s): Chandran, Vinod; McCool, Christopher; Sridharan, Sridha
Contribuinte(s)

Senac, C

Ferrane, I

Data(s)

2006

Resumo

Hybrid face recognition, using image (2D) and structural (3D) information, has explored the fusion of Nearest Neighbour classifiers. This paper examines the effectiveness of feature modelling for each individual modality, 2D and 3D. Furthermore, it is demonstrated that the fusion of feature modelling techniques for the 2D and 3D modalities yields performance improvements over the individual classifiers. By fusing the feature modelling classifiers for each modality with equal weights the average Equal Error Rate improves from 12.60% for the 2D classifier and 12.10% for the 3D classifier to 7.38% for the Hybrid 2D+3D clasiffier.

Identificador

http://eprints.qut.edu.au/25182/

Publicador

University of California

Relação

http://mmua.cs.ucsb.edu/MMUA2006/

Chandran, Vinod, McCool, Christopher, & Sridharan, Sridha (2006) 2D-3D Face Recognition Based on PCA and Feature Modelling. In Senac, C & Ferrane, I (Eds.) Proceedings of the Second International Workshop on MultiModal User Authentication, University of California, Toulouse, France, pp. 1-8.

Fonte

Faculty of Built Environment and Engineering; Information Security Institute; School of Engineering Systems

Palavras-Chave #080106 Image Processing #Image Processing, Multi-Modal, Pattern Recognition, Face Recognition
Tipo

Conference Paper