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Dachapally, P.R. (2017) Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units. arXiv: 1706.01509.
https://doi.org/10.48550/arXiv.1706.01509

has been cited by the following article:

• TITLE: Strengthening the Authentication System of an IP Telephony Network through Facial Emotion Detection

  AUTHORS: Sekoude Jehovah-Nis Pedrie Sonon, Tahirou Djara, Matine Ousmane

  KEYWORDS: Data Security, Emotion Detection, Facial Action Coding System (FACS), Facial Action Units, Facial Recognition

  JOURNAL NAME: Open Journal of Safety Science and Technology, Vol.15 No.1, March 4, 2025

  ABSTRACT: In this paper, we propose an additional layer of security to the authentication system of a VoIP network through the integration of facial emotion detection technology. The proposed system exploits this technology to enhance the existing facial recognition authentication method. Employing computer vision and machine learning techniques, the system captures and analyzes facial expressions during user authentication attempts. Our system takes into account a feasibility assessment prior to implementation and use of the system according to the user’s wishes. The problem we aim to solve is authentication against the user’s will, leaving his or her data vulnerable. We are therefore focusing on the detection of the “fear” emotion and its variants.