Feature fusions for 2.5D face recognition in Random Maxout Extreme Learning Machine

Lee Ying Chong, Thian Song Ong, Andrew Beng Jin Teoh

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)358-372
Number of pages15
JournalApplied Soft Computing Journal
Publication statusPublished - 2019 Feb

Bibliographical note

Funding Information:
This work was supported by the Fundamental Research Grant Scheme (FRGS) of Malaysia [grant numbers MMUE/140021 ] and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NO. 2016R1A2B4011656 ).

Publisher Copyright:
© 2018 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Software


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