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access icon free Multi-perspective gait recognition based on classifier fusion

© The Institution of Engineering and Technology
Received 30/11/2018, Accepted 26/06/2019, Revised 03/06/2019, Published 02/07/2019

Gait recognition has been well known as a promising biometric, which is non-offensive and can identify a person from a distance. In this study, a novel ensemble learning framework for gait recognition, namely multi-perspective gait recognition based on classifier fusion is proposed. Firstly, by utilising bidirectional optical flow, a new algorithm for gait feature extraction is presented, which adaptively extracts the dynamic gait characteristics of walking persons. Secondly, two base classifiers, namely the support vector machine and the hidden Markov model, are trained using the extracted dynamic gait features and traditional gait energy images separately. Thirdly, a novel algorithm is presented for combining two types of base gait classifiers together on the decision level. Finally, the proposed framework by two experiments on the well-known CASIA and OU-ISIR gait databases is evaluated, respectively, and demonstrate the advantages of the proposed methods in comparison with others.

Inspec keywords: support vector machines; hidden Markov models; gait analysis; learning (artificial intelligence); image classification; feature extraction; image fusion; image sequences

Other keywords: base gait classifiers; novel ensemble learning framework; traditional gait energy images; decision level; biometric; classifier fusion; multiperspective gait recognition; bidirectional optical flow; gait feature extraction; dynamic gait characteristics; OU-ISIR gait databases; dynamic gait feature extraction; support vector machine; hidden Markov model; CASIA gait databases

Subjects: Image recognition; Computer vision and image processing techniques; Knowledge engineering techniques; Markov processes; Sensor fusion; Markov processes

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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.6566
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