Intelligent Facial Action and emotion recognition for humanoid robots

Zhang, Li, Hossain, Alamgir and Jiang, Ming (2014) Intelligent Facial Action and emotion recognition for humanoid robots. In: 2014 International Joint Conference on Neural Networks (IJCNN), 6th - 11th July 2014, Beijing.

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Official URL: http://dx.doi.org/10.1109/IJCNN.2014.6889647

Abstract

This research focuses on the development of a realtime intelligent facial emotion recognition system for a humanoid robot. In our system, Facial Action Coding System is used to guide the automatic analysis of emotional facial behaviours. The work includes both an upper and a lower facial Action Units (AU) analyser. The upper facial analyser is able to recognise six AUs including Inner and Outer Brow Raiser, Upper Lid Raiser etc, while the lower facial analyser is able to detect eleven AUs including Upper Lip Raiser, Lip Corner Puller, Chin Raiser, etc. Both of the upper and lower analysers are implemented using feedforward Neural Networks (NN). The work also further decodes six basic emotions from the recognised AUs. Two types of facial emotion recognisers are implemented, NN-based and multi-class Support Vector Machine (SVM) based. The NN-based facial emotion recogniser with the above recognised AUs as inputs performs robustly and efficiently. The Multi-class SVM with the radial basis function kernel enables the robot to outperform the NN-based emotion recogniser in real-time posed facial emotion detection tasks for diverse testing subjects.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: emotion recognition, face recognition, humanoid robots, image coding, intelligent robots, object detection, radial basis function networks, robot vision, support vector machines
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Nov 2014 09:47
Last Modified: 10 Aug 2015 11:03
URI: http://nrl.northumbria.ac.uk/id/eprint/18097

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