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Facial Expression Emotion Detection for Real-Time Embedded Systems

Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK
Department of Electronics and Multimedia Telecommunications, Technical University of Kosice, Letna 9, 04001 Kosice, Slovakia
Author to whom correspondence should be addressed.
This paper is an extended version of our paper in Proceedings of Innovative Computing Technology (INTECH 2017), Luton, UK, 16–18 August 2017; with permission from IEEE.
Technologies 2018, 6(1), 17;
Received: 15 December 2017 / Revised: 13 January 2018 / Accepted: 22 January 2018 / Published: 26 January 2018
Recently, real-time facial expression recognition has attracted more and more research. In this study, an automatic facial expression real-time system was built and tested. Firstly, the system and model were designed and tested on a MATLAB environment followed by a MATLAB Simulink environment that is capable of recognizing continuous facial expressions in real-time with a rate of 1 frame per second and that is implemented on a desktop PC. They have been evaluated in a public dataset, and the experimental results were promising. The dataset and labels used in this study were made from videos, which were recorded twice from five participants while watching a video. Secondly, in order to implement in real-time at a faster frame rate, the facial expression recognition system was built on the field-programmable gate array (FPGA). The camera sensor used in this work was a Digilent VmodCAM — stereo camera module. The model was built on the Atlys™ Spartan-6 FPGA development board. It can continuously perform emotional state recognition in real-time at a frame rate of 30. A graphical user interface was designed to display the participant’s video in real-time and two-dimensional predict labels of the emotion at the same time. View Full-Text
Keywords: FPGA; facial expression analysis; artificial intelligence; real-time implementation FPGA; facial expression analysis; artificial intelligence; real-time implementation
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MDPI and ACS Style

Turabzadeh, S.; Meng, H.; Swash, R.M.; Pleva, M.; Juhar, J. Facial Expression Emotion Detection for Real-Time Embedded Systems. Technologies 2018, 6, 17.

AMA Style

Turabzadeh S, Meng H, Swash RM, Pleva M, Juhar J. Facial Expression Emotion Detection for Real-Time Embedded Systems. Technologies. 2018; 6(1):17.

Chicago/Turabian Style

Turabzadeh, Saeed, Hongying Meng, Rafiq M. Swash, Matus Pleva, and Jozef Juhar. 2018. "Facial Expression Emotion Detection for Real-Time Embedded Systems" Technologies 6, no. 1: 17.

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