Using Face Recognition to Detect “Ghost Writer” Cheating in Examination

Huan He; Qinghua Zheng; Rui Li; Bo Dong

doi:10.1007/978-3-030-23712-7_54

Using Face Recognition to Detect “Ghost Writer” Cheating in Examination

Huan He
, Qinghua Zheng
, Rui Li
, Bo Dong

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Cheating in examinations of the online distance education is a serious problem which may damage the fairness of exam and further undermine the credibility and reputation of certificates. In order to detect the “Ghost Writer” cheating strategy that existed in both online and offline exams, we propose the Student Identification by Face Recognition (SIFR) framework, a three layers architecture based on face recognition technique and micro-service principle, to detect the ghostwriter who takes the exam for others. In addition, we implement a prototype system based on open source projects and public cloud services. To evaluate the system, an experimental test was conducted with public data. The results indicated that the SIFR framework is feasible and the accuracy of detection is directly affected by the performance of face recognition service, which can be upgraded or replaced with better facial feature extraction module.

Original language	English
Title of host publication	E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings
Editors	Abdennour El Rhalibi, Zhigeng Pan, Dandan Ding, Haiyan Jin, Yinghui Wang, Andres A. Navarro-Newball
Publisher	Springer Verlag
Pages	389-397
Number of pages	9
ISBN (Print)	9783030237110
DOIs	https://doi.org/10.1007/978-3-030-23712-7_54
State	Published - 2019
Event	12th International Conference on e-Learning and Games, Edutainment 2018 - Xi'an, China Duration: 28 Jun 2018 → 30 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11462 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on e-Learning and Games, Edutainment 2018
Country/Territory	China
City	Xi'an
Period	28/06/18 → 30/06/18

Keywords

Cheating detection
Face recognition
Ghost writer
Micro-service architecture
Online distance education

Access to Document

10.1007/978-3-030-23712-7_54

Cite this

He, H., Zheng, Q., Li, R., & Dong, B. (2019). Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. In A. El Rhalibi, Z. Pan, D. Ding, H. Jin, Y. Wang, & A. A. Navarro-Newball (Eds.), E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings (pp. 389-397). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11462 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-23712-7_54

He, Huan ; Zheng, Qinghua ; Li, Rui et al. / Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings. editor / Abdennour El Rhalibi ; Zhigeng Pan ; Dandan Ding ; Haiyan Jin ; Yinghui Wang ; Andres A. Navarro-Newball. Springer Verlag, 2019. pp. 389-397 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{6b8c07f8447448daa6e85402dd454528,

title = "Using Face Recognition to Detect “Ghost Writer” Cheating in Examination",

abstract = "Cheating in examinations of the online distance education is a serious problem which may damage the fairness of exam and further undermine the credibility and reputation of certificates. In order to detect the “Ghost Writer” cheating strategy that existed in both online and offline exams, we propose the Student Identification by Face Recognition (SIFR) framework, a three layers architecture based on face recognition technique and micro-service principle, to detect the ghostwriter who takes the exam for others. In addition, we implement a prototype system based on open source projects and public cloud services. To evaluate the system, an experimental test was conducted with public data. The results indicated that the SIFR framework is feasible and the accuracy of detection is directly affected by the performance of face recognition service, which can be upgraded or replaced with better facial feature extraction module.",

keywords = "Cheating detection, Face recognition, Ghost writer, Micro-service architecture, Online distance education",

author = "Huan He and Qinghua Zheng and Rui Li and Bo Dong",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on e-Learning and Games, Edutainment 2018 ; Conference date: 28-06-2018 Through 30-06-2018",

year = "2019",

doi = "10.1007/978-3-030-23712-7\_54",

language = "英语",

isbn = "9783030237110",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "\{El Rhalibi\}, Abdennour and Zhigeng Pan and Dandan Ding and Haiyan Jin and Yinghui Wang and Navarro-Newball, \{Andres A.\}",

booktitle = "E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings",

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He, H, Zheng, Q, Li, R & Dong, B 2019, Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. in A El Rhalibi, Z Pan, D Ding, H Jin, Y Wang & AA Navarro-Newball (eds), E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11462 LNCS, Springer Verlag, pp. 389-397, 12th International Conference on e-Learning and Games, Edutainment 2018, Xi'an, China, 28/06/18. https://doi.org/10.1007/978-3-030-23712-7_54

Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. / He, Huan; Zheng, Qinghua; Li, Rui et al.
E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings. ed. / Abdennour El Rhalibi; Zhigeng Pan; Dandan Ding; Haiyan Jin; Yinghui Wang; Andres A. Navarro-Newball. Springer Verlag, 2019. p. 389-397 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11462 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Face Recognition to Detect “Ghost Writer” Cheating in Examination

AU - He, Huan

AU - Zheng, Qinghua

AU - Li, Rui

AU - Dong, Bo

PY - 2019

Y1 - 2019

N2 - Cheating in examinations of the online distance education is a serious problem which may damage the fairness of exam and further undermine the credibility and reputation of certificates. In order to detect the “Ghost Writer” cheating strategy that existed in both online and offline exams, we propose the Student Identification by Face Recognition (SIFR) framework, a three layers architecture based on face recognition technique and micro-service principle, to detect the ghostwriter who takes the exam for others. In addition, we implement a prototype system based on open source projects and public cloud services. To evaluate the system, an experimental test was conducted with public data. The results indicated that the SIFR framework is feasible and the accuracy of detection is directly affected by the performance of face recognition service, which can be upgraded or replaced with better facial feature extraction module.

AB - Cheating in examinations of the online distance education is a serious problem which may damage the fairness of exam and further undermine the credibility and reputation of certificates. In order to detect the “Ghost Writer” cheating strategy that existed in both online and offline exams, we propose the Student Identification by Face Recognition (SIFR) framework, a three layers architecture based on face recognition technique and micro-service principle, to detect the ghostwriter who takes the exam for others. In addition, we implement a prototype system based on open source projects and public cloud services. To evaluate the system, an experimental test was conducted with public data. The results indicated that the SIFR framework is feasible and the accuracy of detection is directly affected by the performance of face recognition service, which can be upgraded or replaced with better facial feature extraction module.

KW - Cheating detection

KW - Face recognition

KW - Ghost writer

KW - Micro-service architecture

KW - Online distance education

UR - https://www.scopus.com/pages/publications/85073895759

U2 - 10.1007/978-3-030-23712-7_54

DO - 10.1007/978-3-030-23712-7_54

M3 - 会议稿件

AN - SCOPUS:85073895759

SN - 9783030237110

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 389

EP - 397

BT - E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings

A2 - El Rhalibi, Abdennour

A2 - Pan, Zhigeng

A2 - Ding, Dandan

A2 - Jin, Haiyan

A2 - Wang, Yinghui

A2 - Navarro-Newball, Andres A.

PB - Springer Verlag

T2 - 12th International Conference on e-Learning and Games, Edutainment 2018

Y2 - 28 June 2018 through 30 June 2018

ER -

He H, Zheng Q, Li R, Dong B. Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. In El Rhalibi A, Pan Z, Ding D, Jin H, Wang Y, Navarro-Newball AA, editors, E-Learning and Games - 12th International Conference, Edutainment 2018, Proceedings. Springer Verlag. 2019. p. 389-397. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-23712-7_54

Using Face Recognition to Detect “Ghost Writer” Cheating in Examination

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