Abstract
These days’ security plays the greatest role in order to provide safe platforms and surveillance becomes the essential need to provide accurate results in case of security breach. Gait recognition is a biometric technique that does not need human intervention. Through this technique, human can be uniquely identified. Gait is defined as the way human walks (human locomotion) and this can be used as biometric identity because the manner in which every person walks can uniquely categorize person. But there are many challenges like variation in viewpoints, clothing variations, carrying conditions and so on. A novel approach using deep learning is proposed to address this challenge. To address these limits, we give an idea of having multi-view gait-based recognition system, to provide robust system that is capable of handling one camera and subject walking on different angles from 0° to 180° of view. To achieve the results 3D CNN-based model is used in order to obtain spatio-temporal features. Also, the computation capability of algorithm has been enhanced via transfer learning mechanism. To carry out the experiments, OU-ISIR and CASIA-B dataset are used. Once these features are obtained, these are passed into LSTM network to obtain the long-term dependencies from gait sequence. Network is trained and tested and experiment result shows the proposed technique in this paper outperforms the state of art techniques.
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Parashar, A., Parashar, A., Aski, V., Shekhawat, R.S. (2021). Surveillance System to Provide Secured Gait Signatures in Multi-view Variations Using Deep Learning. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_21
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DOI: https://doi.org/10.1007/978-981-15-5243-4_21
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