Emotion recognition - 1
Emotion recognition has become an emphasising research area since it plays an important role in the human computer interaction paradigm. However, emotions can be complex which makes it challenging for machines to perceive various facial expressions. Unlike humans, machines require a much subtle appr...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/70740 |
| _version_ | 1811681610333945856 |
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| author | Muhammad Farhan Othman |
| author2 | Deepu Rajan |
| author_facet | Deepu Rajan Muhammad Farhan Othman |
| author_sort | Muhammad Farhan Othman |
| collection | NTU |
| description | Emotion recognition has become an emphasising research area since it plays an important role in the human computer interaction paradigm. However, emotions can be complex which makes it challenging for machines to perceive various facial expressions. Unlike humans, machines require a much subtle approach in order to classify a facial expression correctly.
This project aims to build a real-time Emotion Recognition program which detects four basic emotions: Happy, Anger, Sadness and Surprise. To achieve the aforementioned, the CK+ database will be used for training the emotion and Action Units classifiers. In addition, the Microsoft Kinect V1 Sensor will be used to track the facial features in real-time. Finally, the detected facial features will be classified to obtain the corresponding emotion and action units.
To evaluate the performance of the Emotion Recognition program, test subjects are required to watch a series of videos which are aimed to evoke the desired emotion. The results are recorded and will be presented in this report. Also, this report will highlight the performance of the classifiers with the different machine learning algorithms such as Naïve-Bayes, Decision-Tree and Support Vector Machine. It was shown that for emotion classification, the one vs one Multi-Class SVM with the Gaussian kernel is most suited. For Action Units classification, a combination of Multi-label and Multiclass SVM with the Gaussian kernel shows acceptable performance. |
| first_indexed | 2024-10-01T03:43:41Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/70740 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:43:41Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/707402023-03-03T20:34:47Z Emotion recognition - 1 Muhammad Farhan Othman Deepu Rajan School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Emotion recognition has become an emphasising research area since it plays an important role in the human computer interaction paradigm. However, emotions can be complex which makes it challenging for machines to perceive various facial expressions. Unlike humans, machines require a much subtle approach in order to classify a facial expression correctly. This project aims to build a real-time Emotion Recognition program which detects four basic emotions: Happy, Anger, Sadness and Surprise. To achieve the aforementioned, the CK+ database will be used for training the emotion and Action Units classifiers. In addition, the Microsoft Kinect V1 Sensor will be used to track the facial features in real-time. Finally, the detected facial features will be classified to obtain the corresponding emotion and action units. To evaluate the performance of the Emotion Recognition program, test subjects are required to watch a series of videos which are aimed to evoke the desired emotion. The results are recorded and will be presented in this report. Also, this report will highlight the performance of the classifiers with the different machine learning algorithms such as Naïve-Bayes, Decision-Tree and Support Vector Machine. It was shown that for emotion classification, the one vs one Multi-Class SVM with the Gaussian kernel is most suited. For Action Units classification, a combination of Multi-label and Multiclass SVM with the Gaussian kernel shows acceptable performance. Bachelor of Engineering (Computer Science) 2017-05-09T08:46:12Z 2017-05-09T08:46:12Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70740 Videos-DRNTU/fyp_17/Emotion Recognition - 1.mp4 en Nanyang Technological University 51 p. application/pdf text/html |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Muhammad Farhan Othman Emotion recognition - 1 |
| title | Emotion recognition - 1 |
| title_full | Emotion recognition - 1 |
| title_fullStr | Emotion recognition - 1 |
| title_full_unstemmed | Emotion recognition - 1 |
| title_short | Emotion recognition - 1 |
| title_sort | emotion recognition 1 |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/70740 |
| work_keys_str_mv | AT muhammadfarhanothman emotionrecognition1 |