3D Facial Action Units Recognition for Emotional Expression
Norhaida Hussain1, Hamimah Ujir2, Irwandi Hipiny3, Jacey-Lynn Minoi4
1Norhaida Hussain, Department of Information Technology and Communication, Politeknik Kuching, Malaysia.
2Hamimah Ujir, Department of Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia.
3Irwandi Hipiny, Department of Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia.
4Jacey-Lynn Minoi, Department of Computer Science and Information Technology, Universiti Malaysia Sarawak, Malaysia.
Manuscript received on 21 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1317-1323 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10610882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1061.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The muscular activities caused the activation of facial action units (AUs) when a facial expression is shown by a human face. This paper presents the methods to recognize AU using a distance feature between facial points which activates the muscles. The seven AU involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterizes a happy and sad expression. The recognition is performed on each AU according to the rules defined based on the distance of each facial point. The facial distances chosen are computed from twelve salient facial points. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase. By using any SVM kernels, it is consistent that AUs that are corresponded to sad expression has a high recognition compared to happy expression. The highest average kernel performance across AUs is 93%, scored by quadratic kernel. Best results for NN across AUs is for AU25 (Lips parted) with lowest CE (0.38%) and 0% incorrect classification.
Keywords: 3D AU Recognition, Facial Action Unit’s Recognition, Facial Expression, Support Vector Machine, Neural Network.
Scope of the Article: Pattern Recognition