Automatic facial expression recognition on smartphone
In a variety of sectors, automatic facial expression recognition (AFER) has seen increased use in recent years. With the success of face detection for unlocking screens on smartphones. Implementing the facial expression recognition (FER) system that can be used on smartphones will enable the develop...
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2023
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sg-ntu-dr.10356-1659472023-04-21T15:37:32Z Automatic facial expression recognition on smartphone Huang, Xiaoyan Lu Shijian School of Computer Science and Engineering Shijian.Lu@ntu.edu.sg Engineering::Computer science and engineering In a variety of sectors, automatic facial expression recognition (AFER) has seen increased use in recent years. With the success of face detection for unlocking screens on smartphones. Implementing the facial expression recognition (FER) system that can be used on smartphones will enable the development of more interesting applications, such as games and other useful mobile applications. However, implementing an AFER application on a smartphone is a challenging task, because traditional human emotion algorithms are usually computationally intensive and only can be implemented offline on a computer. Therefore, this paper presents an AFER mobile application, the FER mobile application is a real-time running on the smartphone with the mobile camera. The proposed FER application is to use a Convolutional Neural Networks (CNN) for classification of six basic emotions plus contempt. For the facial expression detection and features are extracted by HAAR Cascade Classifier and the result of the classification will be displayed on the screen immediately. The experiment shows a result of 69.5% of the test accuracy. The experiment is using the Cohn-Kanade (CK+) dataset which include 593 video sequences from 123 different subjects. Bachelor of Engineering (Computer Science) 2023-04-17T06:06:23Z 2023-04-17T06:06:23Z 2023 Final Year Project (FYP) Huang, X. Y. (2023). Automatic facial expression recognition on smartphone. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165947 https://hdl.handle.net/10356/165947 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Huang, Xiaoyan Automatic facial expression recognition on smartphone |
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In a variety of sectors, automatic facial expression recognition (AFER) has seen increased use in recent years. With the success of face detection for unlocking screens on smartphones. Implementing the facial expression recognition (FER) system that can be used on smartphones will enable the development of more interesting applications, such as games and other useful mobile applications. However, implementing an AFER application on a smartphone is a challenging task, because traditional human emotion algorithms are usually computationally intensive and only can be implemented offline on a computer. Therefore, this paper presents an AFER mobile application, the FER mobile application is a real-time running on the smartphone with the mobile camera. The proposed FER application is to use a Convolutional Neural Networks (CNN) for classification of six basic emotions plus contempt. For the facial expression detection and features are extracted by HAAR Cascade Classifier and the result of the classification will be displayed on the screen immediately. The experiment shows a result of 69.5% of the test accuracy. The experiment is using the Cohn-Kanade (CK+) dataset which include 593 video sequences from 123 different subjects. |
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Lu Shijian |
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Lu Shijian Huang, Xiaoyan |
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Final Year Project |
author |
Huang, Xiaoyan |
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Huang, Xiaoyan |
title |
Automatic facial expression recognition on smartphone |
title_short |
Automatic facial expression recognition on smartphone |
title_full |
Automatic facial expression recognition on smartphone |
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Automatic facial expression recognition on smartphone |
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Automatic facial expression recognition on smartphone |
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automatic facial expression recognition on smartphone |
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Nanyang Technological University |
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2023 |
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https://hdl.handle.net/10356/165947 |
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