Abstract
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.
Highlights
Since the last decade, studies on human facial emotion recognition have revealed that computing models based on regression modeling can produce applicable performance [1]
Each participant’s face video was collected from the Xilinx Spartan-6 LX45 field-programmable gate array (FPGA) and a camera sensor, which was connected to it, and a HDMI output from the FPGA was connected to the computer afterwards; the MATLAB
Acquisition Toolbox used in order to receive and save the real-time video features from the FPGA’s board to use for machine learning purposes
Summary
Studies on human facial emotion recognition have revealed that computing models based on regression modeling can produce applicable performance [1]. Machines such as computers, robots, toys and game consoles will have the capability to perform in such a way as to influence the user in adaptive ways relevant for the client’s mental condition. This is the key knowledge in recently proposed new ideas such as emotional computers, emotion-sensing smart phones and emotional robots [4]. The research presented in this paper is an extension of our previous conference article [5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
