dc.contributor.authorMuhammad Farhan Othman
dc.date.accessioned2017-05-09T08:46:12Z
dc.date.available2017-05-09T08:46:12Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70740
dc.description.abstractEmotion 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.en_US
dc.format.extent51 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleEmotion recognition - 1en_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorDeepu Rajanen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.identifier.videostreamVideos-DRNTU/fyp_17/Emotion Recognition - 1.mp4


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