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Facial emotion detection using deep learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The use of machines to perform different tasks is constantly increasing in society. Providing machines with perception can lead them to perform a great variety of tasks; even very complex ones such as elderly care. Machine perception requires that machines understand about their environment and interlocutor's intention. Recognizing facial emotions might help in this regard. During the development of this work, deep learning techniques have been used over images displaying the following facial emotions: happiness, sadness, anger, surprise, disgust, and fear. In this research, a pure convolutional neural network approach outperformed other statistical methods' results achieved by other authors that include feature engineering. Utilizing convolutional networks involves feature learning; which sounds very promising for this task where defining features is not trivial. Moreover, the network was evaluated using two different corpora: one was employed during network's training and it was also helpful for parameter tuning and for network's architecture definition. This corpus consisted of facial acted emotions. The network providing best classification accuracy results was tested against the second dataset. Even though the network was trained using only one corpus; the network reported auspicious results when tested on a different dataset, which displayed facial non-acted emotions. While the results achieved were not state-of-the-art; the evidence gathered points out deep learning might be suitable to classify facial emotion expressions. Thus, deep learning has the potential to improve human-machine interaction because its ability to learn features will allow machines to develop perception. And by having perception, machines will potentially provide smoother responses, drastically improving the user experience.

Place, publisher, year, edition, pages
2016. , 84 p.
Series
IT, 16040
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-300712OAI: oai:DiVA.org:uu-300712DiVA: diva2:952138
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2016-08-11 Created: 2016-08-11 Last updated: 2016-08-11Bibliographically approved

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