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Identity recognition based on convolutional neural networks using gait data

Faraji, F ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/CSICC52343.2021.9420585
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. As a critical part of any security system, identity recognition has become paramount among researchers. In this regard, several methods are presented while considering various sensors and data. In particular, gait data yields rich information about a person, including some exclusive moving patterns which can be utilized to distinguish between different individuals. On the other hand, convolutional neural networks are proved to be applicable for structured data, especially images. In this article, 12 markers are considered in gathering the gait data, each representing a lower-body joint location. Then, utilizing the gait data in a 2D tensor form, three different convolutional neural networks are trained to recognize the identities. Taking light architectures into account, this approach is implementable in realtime application. The obtained result shows the promising capability of the proposed method being used in identity recognition. © 2021 IEEE
  6. Keywords:
  7. Convolution ; Data yields ; Identity recognition ; Joint locations ; Lower body ; Real-time application ; Structured data ; Tensor forms ; Convolutional neural networks
  8. Source: 26th International Computer Conference, Computer Society of Iran, CSICC 2021, 3 March 2021 through 4 March 2021 ; 2021 ; 9781665412414 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/9420585