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All Journal Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health
Syahid Al Irfan
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Computer Science & IT Education Engineering Public Health Social Sciences Other

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Recognizing Micro Expression Pattern Using Convolutional Neural Networks (CNN) Method During Emotion Regulation Training for Parents in The Pandemic Era Intan Puspitasari; Anton Yudhana; Dewi Eko Wati; Syahid Al Irfan
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 3 No. 2 (2023): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v3i2.68

Abstract

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.
Recognizing Micro Expression Pattern Using Convolutional Neural Networks (CNN) Method During Emotion Regulation Training for Parents in The Pandemic Era Intan Puspitasari; Anton Yudhana; Dewi Eko Wati; Syahid Al Irfan
Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Vol. 3 No. 2 (2023): Proceeding of The International Conference of Inovation, Science, Technology, E
Publisher : Program Studi DIII Rekam Medis dan Informasi Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/icistech.v3i2.68

Abstract

During this pandemic, most of people’s activities are carried out through digital media. Both learning and working processes are using the video-conference platform, a platform deemed effective to facilitate the needs of distance communication. One of the limitations of using video-conference lies in difficulty in understanding emotional conditions based on solely camera video. Hence, speakers generally do not know their interlocutors’ feelings related to the materials being presented. Grounded on this issue, we examined a facial expressions-based emotion recognition tool. Micro expression is one of the micro-languages of communication. Machine learning model developed in this study was Deep Learning with Convolutional Neural Network (CNN). The library that was used was Keras, this was used to recognize micro expression pattern. Additionally, OpenCV was also used for the general face recognition process. Both libraries were operated using Python programming language. The result of the micro expression test involving thirty participants detected three types of facial expression, namely joy, sadness, and anger expression. However, face recognition applied in the present study still needed some improvements, especially for anger and sadness expression. With regard to joy expression, 89% of the expression were recognized. Based on the recorded data, it is necessary to improve the recordings criteria to obtain a clearer expression.
Co-Authors Anton Yudhana Dewi Eko Wati Intan Puspitasari