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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5605
Title: Multimodal Gait Recognition with Inertial Sensor Data and Video Using Evolutionary Algorithm
Authors: Kumar P.
Mukherjee S.
Saini R.
Kaushik P.
Pratim Roy, Partha
Dogra D.P.
Published in: IEEE Transactions on Fuzzy Systems
Abstract: Evolutionary decision fusion has applications in biometric authentication and verification. Gray wolf optimizer (GWO) is one such evolutionary decision fusion approach that can be used to tune the fusion parameters in a multimodal data acquisition system. Human gait is a proven biometric trait with applications in security and authentication. However, acquiring human-gait data can be erroneous due to various factors and multimodal fusion of such erroneous gait data can be challenging. In this paper, we propose a new decision fusion-based approach to solve the above problem. Gait data is recorded simultaneously using motion sensors and visible-light camera. The signals of the motion sensors are modeled using a long short-term memory neural network and corresponding video recordings are processed using a three-dimensional convolutional neural network. GWO has been used to optimize the parameters during fusion. It has been chosen based on the underlying hunting strategy that leads to better approximation of the solution. Interestingly, in our case it converges quicker than other optimization techniques such as genetic algorithm or particle swarm optimization. To test the model, a dataset involving 23 males and females has been recorded while they perform four different types of walks, including, normal walk, fast walk, walking while listening to music, and walking while watching multimedia content on a mobile. An overall accuracy of 91.3% has been recorded across all test scenarios. Results reveal that the proposed study can further be explored to design robust gait biometric systems. © 1993-2012 IEEE.
Citation: IEEE Transactions on Fuzzy Systems (2019), 27(5): 956-965
URI: https://doi.org/10.1109/TFUZZ.2018.2870590
http://repository.iitr.ac.in/handle/123456789/5605
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Biometric
deep learning
gait analysis
gray Wolf optimizer (GWO)
Shadow Motion
ISSN: 10636706
Author Scopus IDs: 57212043589
57211037717
57190288840
57195478446
56880478500
35408975400
Author Affiliations: Kumar, P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, 247667, India
Mukherjee, S., Department of Electronics and Communication Engineering, Institute of Engineering and Management, Kolkata, 700 091, India
Saini, R., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, 247667, India
Kaushik, P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, 247667, India
Roy, P.P., Department of Computer Science and Engineering, IIT Roorkee, Roorkee, 247667, India
Dogra, D.P., School of Electrical Sciences, IIT Bhubaneswar, Bhubaneswar, 751013, India
Corresponding Author: Kumar, P.; Department of Computer Science and Engineering, IIT RoorkeeIndia; email: pradeep.iitr7@gmail.com
Appears in Collections:Journal Publications [CS]

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