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DeAlcala, D., Serna, I., Morales, A., Fierrez, J. and Ortega-Garcia, J. (2023) Measuring Bias in AI Models: A Statistical Approach Introducing N-Sigma. 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Torino, 26-30 June 2023, 1167-1172.
https://ieeexplore.ieee.org/abstract/document/10197052/
https://doi.org/10.1109/COMPSAC57700.2023.00176

has been cited by the following article:

• TITLE: Formation of an Original Database and Development of Innovative Deep Learning Algorithms for Detecting Face Impersonation in Online Exams

  AUTHORS: Konan Yao, Tiémoman Kone, Venance Saho Zoh

  KEYWORDS: Online Exams, Face Recognition, Convolutional Neural Networks, Data, Bias

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.13 No.12, December 6, 2023

  ABSTRACT: The issue related to the risk of identity impersonation, where one person can be replaced by another in online exam surveillance systems, poses challenges. This study focuses on the effectiveness of detecting attempts of identity impersonation through face substitution during online exams, with the aim of ensuring the integrity of assessments. The goal is to develop facial recognition algorithms capable of precisely detecting these impersonations, training them on a tailored database rather than biased generic data. An original database of student faces has been created. An algorithm leveraging advanced deep learning techniques such as depthwise separable convolution has been developed and evaluated on this database. We achieved very high levels of precision, reaching an accuracy rate of 98% in face detection and recognition.