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. 2024 Oct 23;19(10):e0310887.
doi: 10.1371/journal.pone.0310887. eCollection 2024.

Stacked-gait: A human gait recognition scheme based on stacked autoencoders

Asif Mehmood  1 , Javeria Amin  2 , Muhammad Sharif  1 , Seifedine Kadry  3   4 , Jungeun Kim  5
Affiliations

Stacked-gait: A human gait recognition scheme based on stacked autoencoders

Asif Mehmood et al. PLoS One. .

Abstract

Human gait recognition (HGR) is the mechanism of biometrics that authors extensively employ to recognize an individuals based on their walking traits. HGR has been prominent for the past few years due to its surveillance capability. In HGR, an individual's walking attributes are utilized for identification. HGR is considered a very effective technique for recognition but faces different problematic factors that degrade its performance. The major factors are variations in clothing, carrying, walking, etc. In this paper, a new hybrid method for the classification of HGR is designed called Stacked-Gait. The system is based on six major steps; initially, image resizing is performed to overcome computation problems. In the second step, these images are converted into grey-scale to extract better features. After that, the dataset division is performed into train and test set. In the next step, the training of the autoencoders and feature extraction of the dataset are performed using training data. In the next step, the stacking of two autoencoders is also performed. After that, the stacked encoders are employed to extract features from the test data. Finally, the feature vectors are given as input to various machine learning classifiers for final classification. The method assessment is performed using the CASIA-B dataset and achieved the accuracy of 99.90, 98.10, 97.20, 97.20, 96.70, and 100 percent on 000, 180, 360, 540, 720, and 900 angles. It is pragmatic that the system gives promising results compared to recent schemes.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Proposed stacked-gait framework for HGR.
Fig 2
Fig 2. Architecture of the autoencoder.
Fig 3
Fig 3. (a) Confusion Matrix for LD using subset-1 (b) Confusion Matrix for SD using subset-1.
Fig 4
Fig 4. (a) Confusion Matrix for LD using subset-2 (b) Confusion Matrix for SD using subset-2.
Fig 5
Fig 5. (a) Confusion Matrix for LD using subset-3 (b) Confusion Matrix for SD using subset-3.
Fig 6
Fig 6. Confusion matrix of proposed classification results at 000 angle.
Fig 7
Fig 7. Confusion matrix of proposed classification results at 180 angle.
Fig 8
Fig 8. Confusion matrix of proposed classification results at 360 angle.
Fig 9
Fig 9. Confusion matrix of proposed classification results at 540 angle.
Fig 10
Fig 10. Confusion matrix of proposed classification results at 720 angle.
Fig 11
Fig 11. Confusion matrix of proposed classification results at 900 angle.
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