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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5549
Title: Don't just sign use brain too: A novel multimodal approach for user identification and verification
Authors: Saini R.
Kaur B.
Singh P.
Kumar P.
Pratim Roy, Partha
Raman, Balasubramanian
Singh D.
Published in: Information Sciences
Abstract: In this paper, we propose a novel multimodal user identification and verification scheme combining two inter-linked biometric traits, i.e., signature and brain signals (Electroencephalography (EEG)). Moulding handwritten strokes for signature stimulates EEG signals in the brain. The response of the brain signals during signing is unique for each user and this brain signal-signature relation is utilized in the proposed scheme. To the best of our knowledge, there exist no authentication approach combining these two inter-linked biometric traits. The proposed multimodal scheme uses Hidden Markov Model (HMM) based sequential classifier to model features extracted from signatures and EEG signals individually. Pyramid Histogram of Orientation Gradients (PHOG) features are extracted from the signature-images and next PHOG features are used to build user specific signature-HMM models. Similarly, user wise EEG-HMM models are built with Daubechies-4 (DB4) wavelet analysis. Finally, a score combining classification scores of signature-HMM and EEG-HMM models is used to perform user identification and verification. To evaluate the effectiveness of the proposed scheme, we have developed a dataset collecting these two traits simultaneously using the Emotiv Epoc+ device and pen-paper for 70 individual subjects. Thereafter, user's identification is performed with individuals’ signature and EEG signals as well as their combined traits. The identification accuracy of the proposed multimodal approach has been achieved upto 98.24%. The effectiveness of the verification scheme is validated by the Receiver Operating Characteristic (ROC) curves. © 2017 Elsevier Inc.
Citation: Information Sciences (2018), 430-431(): 163-178
URI: https://doi.org/10.1016/j.ins.2017.11.045
http://repository.iitr.ac.in/handle/123456789/5549
Issue Date: 2018
Publisher: Elsevier Inc.
Keywords: Electroencephalography (EEG)
Hidden Markov Model (HMM)
Identification
Multimodal data
Signatures
Verification
ISSN: 200255
Author Scopus IDs: 57190288840
57208659024
57212591690
57212043589
56880478500
23135470700
57209912206
Author Affiliations: Saini, R., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Kaur, B., Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Sonepat, India
Singh, P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Kumar, P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Raman, B., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Singh, D., Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Sonepat, India
Corresponding Author: Kumar, P.; Department of Computer Science and Engineering, Indian Institute of TechnologyIndia; email: pra14.dcs2014@iitr.ac.in
Appears in Collections:Journal Publications [CS]

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