research-article

Fusion of face recognition and facial expression detection for authentication: a proposed model

Authors:

Delina Beh Mei Yin,

Bazilah A. Talip,

Nur Afiqah Mohd Norain,

Abu Talib OthmanAuthors Info & Claims

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

Article No.: 21, Pages 1 - 8

https://doi.org/10.1145/3022227.3022247

Published: 05 January 2017 Publication History

Abstract

The paper presents a novel model of hybrid biometric-based authentication. Currently, the recognition accuracy of a single biometric verification system is often much reduced due to many factors such as the environment, user mode and physiological defects of an individual. Apparently, the enrolment of static biometric is highly vulnerable to impersonation attack. Due to the fact of single biometric authentication only offers one factor of verification, we proposed to hybrid two biometric attributes that consist of physiological and behavioural trait. In this study, we utilise the static and dynamic features of a human face. In order to extract the important features from a face, the primary steps taken are image pre-processing and face detection. Apparently, to distinguish between a genuine user or an imposter, the first authentication is to verify the user's identity through face recognition. Solely depending on a single modal biometric is possible to lead to false acceptance when two or more similar face features may result in a relatively high match score. However, it is found the False Acceptance Rate is 0.55% whereas the False Rejection Rate is 7%. By reason of the security discrepancies in the mentioned condition, therefore we proposed a fusion method whereby a genuine user will select a facial expression from the seven universal expression (i.e. happy, sad, anger, disgust, surprise, fear and neutral) as enrolled earlier in the database. For the proof of concept, it is proven in our results that even there are two or more users coincidently have the same face features, the selected facial expression will act as a password to be prominently distinguished a genuine or impostor user.

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Cited By

Stratton DHand E(2022)Bridging the Gap Between Automated and Human Facial Emotion Perception2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00268(2400-2410)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00268
Lasri IRiadsolh AElbelkacemi M(2022)Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learningEducation and Information Technologies10.1007/s10639-022-11370-428:4(4069-4092)Online publication date: 11-Oct-2022
https://doi.org/10.1007/s10639-022-11370-4
Balakrishnan Unny R. Bhatt N(2021)A Meta-Analysis of PrivacyInternational Journal of Information Security and Privacy10.4018/IJISP.28558016:1(1-22)Online publication date: 26-Nov-2021
https://doi.org/10.4018/IJISP.285580
Show More Cited By

Index Terms

Fusion of face recognition and facial expression detection for authentication: a proposed model
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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cover image ACM Conferences

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

January 2017

746 pages

ISBN:9781450348881

DOI:10.1145/3022227

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 January 2017

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IMCOM '17

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SIGAPP

IMCOM '17: The 11th International Conference on Ubiquitous Information Management and Communication

January 5 - 7, 2017

Beppu, Japan

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IMCOM '17 Paper Acceptance Rate 113 of 366 submissions, 31%;

Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Stratton DHand E(2022)Bridging the Gap Between Automated and Human Facial Emotion Perception2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00268(2400-2410)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00268
Lasri IRiadsolh AElbelkacemi M(2022)Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learningEducation and Information Technologies10.1007/s10639-022-11370-428:4(4069-4092)Online publication date: 11-Oct-2022
https://doi.org/10.1007/s10639-022-11370-4
Balakrishnan Unny R. Bhatt N(2021)A Meta-Analysis of PrivacyInternational Journal of Information Security and Privacy10.4018/IJISP.28558016:1(1-22)Online publication date: 26-Nov-2021
https://doi.org/10.4018/IJISP.285580
Mei Yin DMukhlas AWan Chik RTalib Othman AOmar S(2018)A Proposed Approach for Biometric-Based Authentication Using of Face and Facial Expression Recognition2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS)10.1109/ICOMIS.2018.8644974(28-33)Online publication date: Dec-2018
https://doi.org/10.1109/ICOMIS.2018.8644974
Bonny TRabie THafez A(2018)Multiple histogram-based face recognition with high speed FPGA implementationMultimedia Tools and Applications10.1007/s11042-018-5647-877:18(24269-24288)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11042-018-5647-8

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