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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15666
Title: Air Signature Recognition Using Deep Convolutional Neural Network-Based Sequential Model
Authors: Behera S.K.
Dash A.K.
Dogra D.P.
Pratim Roy, Partha
Published in: Proceedings of International Conference on Pattern Recognition
Abstract: Deep convolutional neural networks are becoming extremely popular in classification, especially when the inputs are non-sequential in nature. Though it seems unrealistic to adopt such networks as sequential classifiers, however, researchers have started to use them for applications that primarily deal with sequential data. It is possible, if the sequential data can be represented in the conventional way the inputs are provided in CNNs. Signature recognition is one of the important tasks for biometric applications. Signatures represent the signer's identity. Air signatures can make traditional biometric systems more secure and robust than conventional pen-paper or stylus guided interfaces. In this paper, we propose a new set of geometrical features to represent 3D air signatures captured using Leap motion sensor. The features are then arranged such that they can be fed to a deep convolutional neural network architecture with application specific tuning of the model parameters. It has been observed that the proposed features in combination with the CNN architecture can act as a good sequential classifier when tested on a moderate size air signature dataset. Experimental results reveal that the proposed biometric system performs better as compared to the state-of-the-art geometrical features with average accuracy improvement of 4%. © 2018 IEEE.
Citation: Proceedings of International Conference on Pattern Recognition, (2018), 3525- 3530
URI: https://doi.org/10.1109/ICPR.2018.8546265
http://repository.iitr.ac.in/handle/123456789/15666
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Biometrics
Classification (of information)
Convolution
Network architecture
Neural networks
Accuracy Improvement
Application specific
Biometric applications
Deep convolutional neural networks
Geometrical features
Sequential classifier
Signature recognition
Signer's identity
Deep neural networks
ISBN: 9.78154E+12
ISSN: 10514651
Author Scopus IDs: 37057132600
56723571400
35408975400
56880478500
Author Affiliations: Behera, S.K., School of Electrical Sciences Indian Institute of Technology, Bhubaneswar, 752050, India
Dash, A.K., School of Electrical Sciences Indian Institute of Technology, Bhubaneswar, 752050, India
Dogra, D.P., School of Electrical Sciences Indian Institute of Technology, Bhubaneswar, 752050, India
Roy, P.P., Department Computer Science Engineering, Indian Institute of Technology, Roorkee, 247667, India
Appears in Collections:Conference Publications [CS]

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