Multimodal 2d + 3d multi-descriptor tensor for face verification

In the last few years, there is a growing interest in multilinear subspace learning for dimensionality reduction of multidimensional data. In this paper, we proposed a multimodal 2D + 3D face verification system based on Multilinear Discriminant Analysis MDA integrating Within Class Covariance Norma...

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Bibliographic Details
Published in:Multimedia tools and applications 2020-08, Vol.79 (31-32), p.23071-23092
Main Authors: Saoud, Adel, Oumane, Abdelmalik, Ouafi, Abdelkrim, Taleb-Ahmed, Abdelmalik
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Communication Networks
Computer Science
Covariance
Data Structures and Information Theory
Discriminant analysis
Electronics
Engineering Sciences
Face recognition
Feature extraction
Histograms
Mathematical analysis
Multidimensional data
Multimedia Information Systems
Networking and Internet Architecture
Signal and Image processing
Special Purpose and Application-Based Systems
Tensors
Verification
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Summary:In the last few years, there is a growing interest in multilinear subspace learning for dimensionality reduction of multidimensional data. In this paper, we proposed a multimodal 2D + 3D face verification system based on Multilinear Discriminant Analysis MDA integrating Within Class Covariance Normalization WCCN technique. Histograms of local descriptor applied to features extraction from 2D and 3D face images are concatenated and organized as a tensor design. This tensor is then reduced and projected using MDA technique into a lower subspace. WCCN technique is used to reduce the effect of the intra class directions using normalisation transform and to enhance the discrimination power of the MDA. Our experiments were carried out on the three biggest databases: FRGC v2.0, Bosphorus and CASIA 3D under expressions, occlusions and pose variations. Experimental results showed the superiority of the proposed approach in term of verification rate when compared to the state of the art method.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09095-y