Sparse tensor discriminative locality alignment for gait recognition

Nengwen Zhao, Lefei Zhang, Bo Du, Liangpei Zhang, Dacheng Tao, Jia You

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and subsequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
Number of pages7
ISBN (Electronic)9781509006199
Publication statusPublished - 31 Oct 2016
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver Convention Centre, Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016


Conference2016 International Joint Conference on Neural Networks, IJCNN 2016

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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