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|Title:||Emotion recognition using machine learning techniques for robots||Authors:||Wang, Yiming||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2017||Abstract:||Emotion Recognition is one of the classification tasks in the computer vision, carrying interactive communication between human and machines. This project aims to set up an emotion recognition system in a household robot. The recognition system is realized by balancing the factors in terms of hardware constraint and recognition accuracy. More specifically, the household robot is supposed to conduct a few tasks but within a limited 2GB memory space, therefore, the software must be designed memory-compactly. As for the real-time test using a webcam, the model first tries to capture faces in a video frame by using the HOG feature face detector, then it applies several preprocessing techniques such as Gaussian blurring, adaptive histogram equalization and mean- subtraction to the face and then sends it to the pre-trained smaller AlexNet model for the recognition task. The test accuracy of the model reaches 0.71 and the highest recognition rate reaches 0.90 for a happy face on FER-2013 test set.||URI:||http://hdl.handle.net/10356/70949||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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