Gait recognition and micro-expression recognition based on maximum margin projection with tensor representation. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Ben, Xianye; Zhang, Peng; Yan, Rui; Yang, Mingqiang; Ge, Guodong
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2629
• Page End: 2646
• Publication Date: 2016-11
• Subjects: Maximum margin projection with tensor representation (MMPTR) -- Dimensionality reduction -- Gait recognition -- Micro-expression recognition; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2031-8
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract We contribute, through this paper, to design a novel algorithm called maximum margin projection with tensor representation (MMPTR). This algorithm is able to recognize gait and micro-expression represented as third-order tensors. Through maximizing the inter-class Laplacian scatter and minimizing the intra-class Laplacian scatter, MMPTR can seek a tensor-to-tensor projection that directly extracts discriminative and geometry-preserving features from the original tensorial data. We show the validity of MMPTR through extensive experiments on the CASIA(B) gait database, TUM GAID gait database, and CASME micro-expression database. The proposed MMPTR generally obtains higher accuracy than MPCA, GTDA as well as state-of-the-art DTSA algorithm. Experimental results included in this paper suggest that MMPTR is especially effective in such tensorial object recognition tasks.