Gait-DenseNet: A Hybrid Convolutional Neural Network for Gait Recognition

Jashila Nair Mogan, Chin Poo Lee, Kian Ming Lim

Research output: Journal PublicationArticlepeer-review

10 Citations (Scopus)

Abstract

Gait is the walking posture of a human, which involves movements of joints at upper limbs and lower limbs of the body. In gait recognition, the human appearance changes are taken into account, which makes it easier to differentiate every individual. However, covariates such as viewing angle, clothing and carrying condition act as the crucial factors that affect the gait recognition process. In this work, a hybrid model that integrates pre-trained DenseNet-201 and multilayer perceptron is presented. The method first extracts the gait energy image by windowing the gait binary images. Subsequently, transfer learning of the pre-trained DenseNet-201 model is leveraged to learn the representative features of the gait energy image. A multilayer perceptron is then used to further capture the relationships between these features. Finally, a classification layer assigns the features to the associated class label. The performance of the proposed method is evaluated on CASIA-B dataset, OU-ISIR D dataset and OU-ISIR Large Population dataset.

Original languageEnglish
Article numberIJCS_49_2_13
JournalIAENG International Journal of Computer Science
Volume49
Issue number2
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Convolutional neural network
  • Densenet-201
  • Gait recognition
  • Gei
  • Multilayer perceptron

ASJC Scopus subject areas

  • General Computer Science

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