<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.</p></abstract>

The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.

3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

Department of Electrical and Electronic Engineering

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms"}

The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In...

3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

Data publikacji: 18 cze 2019

Zakres stron: 28 - 37

Otrzymano: 10 mar 2019

Przyjęty: 19 kwi 2019

DOI: https://doi.org/10.2478/cait-2019-0013

Słowa kluczowe
Face recognition, clustering, FRGCv2 dataset, non-negative matrix factorisation

© 2019 Hawraa H. Abbas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

Data publikacji: 18 cze 2019

Zakres stron: 28 - 37

Otrzymano: 10 mar 2019

Przyjęty: 19 kwi 2019

DOI: https://doi.org/10.2478/cait-2019-0013

Słowa kluczoweFace recognition, clustering, FRGCv2 dataset, non-negative matrix factorisation

© 2019 Hawraa H. Abbas et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Face recognition, clustering, FRGCv2 dataset, non-negative matrix factorisation