Abstract
Walking speed is used principally to assess the status of human health. Recent gait recognition systems often experience difficulties, including variations in the viewing angle and enormous variations in the intra-class. For decades, computer vision-based approaches are in great demand and more effective in clinical gait analysis. Used referenced dataset is under well-controlled conditions. Gait movement rate is the predominant human biomechanical determinant. However, owing to occlusion, there could be chance of incomplete gait cycle data. To improve the gait identification accuracy from an incomplete gait dataset, we proposed the generative adversarial network (GAN). To be more specific, network will restore full GEIs from partial GEIs. The proposed generator structure helps to create complete GEIs from partial gait energy image and two discriminator structures; one of them dictates if a obtained image is a whole GEI and the other if two GEI identical to the ground truth gait. We tested our method on the CASIA-B gait cycle dataset, and the structure effectively rebuilds partial gait energy image from the most severe partial gait periods.
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Bharti, J., Roy, B.N., Lohiya, L. (2022). Reconstruction of Partial Gait Cycle and Identification. In: Pandian, A.P., Palanisamy, R., Narayanan, M., Senjyu, T. (eds) Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-7330-6_4
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DOI: https://doi.org/10.1007/978-981-16-7330-6_4
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