Citation

Chong, Lee Ying and Ong, Thian Song and Teoh, Andrew Beng Jin (2019) Feature fusions for 2.5D face recognition in Random Maxout Extreme Learning Machine. Applied Computing and Informatics, 75. pp. 358-372. ISSN 1568-4946, 1872-9681

Text 5.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Contemporary face recognition system is often based on either 2D (texture) or 3D (texture + shape) face modality. An alternative modality that utilizes range (depth) facial images, namely 2.5D face recognition emerges. In this paper, we propose a 2.5D face descriptor that based on the Regional Covariance Matrix (RCM), a powerful means of feature fusion technique and a novel classifier dubbed Random Maxout Extreme Learning Machine (RMELM). The RCM of interest is constructed based on the Principal Component Analysis (PCA) filters responses of facial texture and/or range image, wherein the PCA filters are learned from a two-layer PCA network. The RMELM is an ELM variant where the activation function is based on the locally linear maxout function, in place of typical global non-linear functions in ELM. Since the RCM is a special case of symmetric positive definite matrix that resides on the Tensor manifold; a gap exists in between RCM and RMELM, which is a vector-based classifier. To bridge the gap, we flatten the manifold by transforming the RCM to a feature vector via a matrix logarithm operator. Experimental results from two public 3D face databases, FRGC v2.0 database and Gavab database, validated our proposed method is promising in 2.5D face recognition.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, Tensor manifold, Regional covariance matrix, Fusion, Random maxout, Extreme learning machine
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	15 Sep 2021 02:58
Last Modified:	15 Sep 2021 02:58
URII:	http://shdl.mmu.edu.my/id/eprint/8920

Downloads

Edit (login required)