Fusion-Based 2.5D Face Recognition System

Min-Er Teo; Lee-Ying Chong; Siew-Chin Chong

doi:10.18080/jtde.v12n1.770

Min-Er Teo

Multimedia University, Malaysia

https://orcid.org/0009-0006-9344-7267
Lee-Ying Chong

Multimedia University, Malaysia

https://orcid.org/0000-0002-4957-6460
Siew-Chin Chong

Multimedia University, Malaysia

https://orcid.org/0000-0003-0421-4367

Keywords

fusion-based approach, depth image, 2.5D data, Gabor-based Region Covariance Matrices, 2.5D face recognition

Abstract

Face recognition is the dominant biometrics system used to authenticate an individual’s identity in various applications. Most commercial face recognition systems rely on 2D face images, but the changes in the environment lighting and a person's posture affect the accuracy of the 2D face recognition systems. Hence, the 2.5D face recognition system arises as the solution to eliminate the drawbacks of the 2D face recognition system. The depth feature in the 2.5D data (depth image) provides additional information that can help to improve the accuracy and robustness of 2.5D face recognition systems, particularly in challenging scenarios. This paper proposes a fusion-based approach for the 2.5D face recognition system to enhance the system’s performance, where feature fusion involves the combination of features extracted from the depth image. Gabor-based Region Covariance Matrices (GRCMs) that serve as face identifiers combine the depth and texture images in the structure of a covariance matrix. Several experiments on different fusions have been conducted in the Face Recognition Grand Challenge version 2 (FRGC v2.0) database. This paper shows that the max-min fusion applied to the surface normal (y-direction) and the mean curvature has achieved the best accuracy rate of 93.66% among the other fusion approaches used.

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770-PDF-v12n1pp19-38 (AUD 30)

Published

Mar 27, 2024

DOI https://doi.org/10.18080/jtde.v12n1.770

How to Cite

Teo, M.-E., Chong, L.-Y., & Chong, S.-C. (2024). Fusion-Based 2.5D Face Recognition System. Journal of Telecommunications and the Digital Economy, 12(1), 19–38. https://doi.org/10.18080/jtde.v12n1.770