An Online Experiment During COVID-19: Testing the Influences of Autonomy Support Toward Emotions and Academic Persistence

Students’ academic persistence is a critical component of effective online learning. Promoting students’ academic persistence could potentially alleviate learning loss or drop-out, especially during challenging time like the COVID-19 pandemic. Previous research indicated that different emotions and autonomy support could all influence students’ academic persistence. However, few studies examined the multidimensionality of persistence using an experimental design with students’ real-time emotions. Using an experimental design and the Contain Intelligent Facial Expression Recognition System (CIFERS), this research explored the dynamic associations among real-time emotions (joy and anxiety), autonomy support (having choice and no choice), self-perceived persistence, self-reliance persistence, and help-seeking persistence. 177 college students participated in this study online via Zoom during COVID-19 university closure. The results revealed that having choice and high intensity of joy could promote students’ self-reliance persistence, but not help-seeking persistence. Interestingly, students who perceived themselves as more persistent experienced more joy during experiment. The theoretical and practical implications on facilitating students’ academic persistence were discussed.

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Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.5.2.480 ◽

2021 ◽

Vol 5 (2) ◽

Author(s):

Hady Pranoto ◽

Oktaria Kusumawardani

Keyword(s):

Face Recognition ◽

Real Time ◽

Success Factors ◽

Recognition System ◽

Expression Recognition ◽

Student Attendance ◽

Face Recognition System ◽

Triplet Loss ◽

Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

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Real Time Facial Expression Recognition System Based on Deep Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1591.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 4047-4051

Keyword(s):

Deep Learning ◽

Facial Expression ◽

Real Time ◽

Facial Expression Recognition ◽

Recognition System ◽

Expression Recognition ◽

Or Education ◽

Face Expression ◽

Active Research ◽

Main Components

The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..

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A Robotic Facial Expression Recognition System Using Real-Time Vision System

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.381-382.375 ◽

2008 ◽

Vol 381-382 ◽

pp. 375-378

Author(s):

K.T. Song ◽

M.J. Han ◽

F.Y. Chang ◽

S.H. Chang

Keyword(s):

Image Processing ◽

Facial Expression ◽

Real Time ◽

Facial Expressions ◽

Facial Expression Recognition ◽

Vision System ◽

Recognition System ◽

Expression Recognition ◽

On Line ◽

Processing Platform

The capability of recognizing human facial expression plays an important role in advanced human-robot interaction development. Through recognizing facial expressions, a robot can interact with a user in a more natural and friendly manner. In this paper, we proposed a facial expression recognition system based on an embedded image processing platform to classify different facial expressions on-line in real time. A low-cost embedded vision system has been designed and realized for robotic applications using a CMOS image sensor and digital signal processor (DSP). The current design acquires thirty 640x480 image frames per second (30 fps). The proposed emotion recognition algorithm has been successfully implemented on the real-time vision system. Experimental results on a pet robot show that the robot can interact with a person in a responding manner. The developed image processing platform is effective for accelerating the recognition speed to 25 recognitions per second with an average on-line recognition rate of 74.4% for five facial expressions.

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A Face Based Real Time Communication for Physically and Speech Disabled People

Assistive Technologies ◽

10.4018/978-1-4666-4422-9.ch075 ◽

2013 ◽

pp. 1434-1460

Author(s):

Ong Chin Ann ◽

Marlene Valerie Lu ◽

Lau Bee Theng

Keyword(s):

Facial Expression ◽

Real Time ◽

Face Detection ◽

Facial Expression Recognition ◽

Recognition System ◽

Detection Algorithm ◽

Expression Recognition ◽

Time Behaviour ◽

The Disabled ◽

Detection Stage

The main purpose of this research is to enhance the communication of the disabled community. The authors of this chapter propose an enhanced interpersonal-human interaction for people with special needs, especially those with physical and communication disabilities. The proposed model comprises of automated real time behaviour monitoring, designed and implemented with the ubiquitous and affordable concept in mind to suit the underprivileged. In this chapter, the authors present the prototype which encapsulates an automated facial expression recognition system for monitoring the disabled, equipped with a feature to send Short Messaging System (SMS) for notification purposes. The authors adapted the Viola-Jones face detection algorithm at the face detection stage and implemented template matching technique for the expression classification and recognition stage. They tested their model with a few users and achieved satisfactory results. The enhanced real time behaviour monitoring system is an assistive tool to improve the quality of life for the disabled by assisting them anytime and anywhere when needed. They can do their own tasks more independently without constantly being monitored physically or accompanied by their care takers, teachers, or even parents. The rest of this chapter is organized as follows. The background of the facial expression recognition system is reviewed in Section 2. Section 3 is the description and explanations of the conceptual model of facial expression recognition. Evaluation of the proposed system is in Section 4. Results and findings on the testing are laid out in Section 5, and the final section concludes the chapter.

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