Real-time emotion detection
Deep learning dominates the field of computer vision in recent years and in every few weeks a new deep learning technology takes over the other. Herein, convolutional neural network (CNN) is applied in this project. Detecting facial expressions have been a very fast-growing topic in the field of co...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/76147 |
| _version_ | 1811684176459464704 |
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| author | Koh, Melvyn Nguan Theng |
| author2 | Althea Liang Qianhui |
| author_facet | Althea Liang Qianhui Koh, Melvyn Nguan Theng |
| author_sort | Koh, Melvyn Nguan Theng |
| collection | NTU |
| description | Deep learning dominates the field of computer vision in recent years and in every few weeks a new deep learning technology takes over the other. Herein, convolutional neural network (CNN) is applied in this project.
Detecting facial expressions have been a very fast-growing topic in the field of computer vision as facial expressions are seen as a significant role in human communication and behavioural analysis. Ever since Paul Ekman devised the Facial Action Coding System (FACS) to detect a human facial feature and model the facial behaviours, many scientists are inspired to conduct psychological research on detecting real emotions of a person. Therefore, this has in turn inspired computer scientists to conduct tremendous active research in this field – finding the most accurate and fast models to detecting the true emotion of a person with a camera. This involves using Extended Cohn-Kanade (CK+) and FER2013 datasets.
This project aims to build a Real-Time Emotion Detection application that detects seven emotions namely – Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral.
The software application is written in Python programming language with OpenCV for processing images and videos. CNN-based approach is done with Google’s Tensorflow machine-learning library to construct the trained model. Lastly, Keras is used as the high-level neural networks API (application programming interface) that runs on top of Tensorflow. The model is trained and evaluated on the FER2013 and CK+ datasets. |
| first_indexed | 2024-10-01T04:24:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/76147 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:24:28Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/761472023-03-03T20:45:25Z Real-time emotion detection Koh, Melvyn Nguan Theng Althea Liang Qianhui School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Deep learning dominates the field of computer vision in recent years and in every few weeks a new deep learning technology takes over the other. Herein, convolutional neural network (CNN) is applied in this project. Detecting facial expressions have been a very fast-growing topic in the field of computer vision as facial expressions are seen as a significant role in human communication and behavioural analysis. Ever since Paul Ekman devised the Facial Action Coding System (FACS) to detect a human facial feature and model the facial behaviours, many scientists are inspired to conduct psychological research on detecting real emotions of a person. Therefore, this has in turn inspired computer scientists to conduct tremendous active research in this field – finding the most accurate and fast models to detecting the true emotion of a person with a camera. This involves using Extended Cohn-Kanade (CK+) and FER2013 datasets. This project aims to build a Real-Time Emotion Detection application that detects seven emotions namely – Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral. The software application is written in Python programming language with OpenCV for processing images and videos. CNN-based approach is done with Google’s Tensorflow machine-learning library to construct the trained model. Lastly, Keras is used as the high-level neural networks API (application programming interface) that runs on top of Tensorflow. The model is trained and evaluated on the FER2013 and CK+ datasets. Bachelor of Engineering (Computer Engineering) 2018-11-20T08:46:17Z 2018-11-20T08:46:17Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/76147 en Nanyang Technological University 47 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Koh, Melvyn Nguan Theng Real-time emotion detection |
| title | Real-time emotion detection |
| title_full | Real-time emotion detection |
| title_fullStr | Real-time emotion detection |
| title_full_unstemmed | Real-time emotion detection |
| title_short | Real-time emotion detection |
| title_sort | real time emotion detection |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/76147 |
| work_keys_str_mv | AT kohmelvynnguantheng realtimeemotiondetection |