Combining deep neural network with traditional classifier to recognize facial expressions
Fei, Zixiang and Yang, Erfu and Li, David and Butler, Stephen and Ijomah, Winifred and Zhou, Huiyu; (2019) Combining deep neural network with traditional classifier to recognize facial expressions. In: 2019 25th IEEE International Conference on Automation and Computing. IEEE, GBR. ISBN 9781861376664 (https://doi.org/10.23919/IConAC.2019.8895084)
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Abstract
Facial expressions are important in people's daily communications. Recognising facial expressions also has many important applications in the areas such as healthcare and e-learning. Existing facial expression recognition systems have problems such as background interference. Furthermore, systems using traditional approaches like SVM (Support Vector Machine) have weakness in dealing with unseen images. Systems using deep neural network have problems such as requirement for GPU, longer training time and requirement for large memory. To overcome the shortcomings of pure deep neural network and traditional facial recognition approaches, this paper presents a new facial expression recognition approach which has image pre-processing techniques to remove unnecessary background information and combines deep neural network ResNet50 and a traditional classifier-the multiclass model for Support Vector Machine to recognise facial expressions. The proposed approach has better recognition accuracy than traditional approaches like Support Vector Machine and doesn't need GPU. We have compared 3 proposed frameworks with a traditional SVM approach against the Karolinska Directed Emotional Faces (KDEF) Database, the Japanese Female Facial Expression (JAFFE) Database and the extended Cohn-Kanade dataset (CK+), respectively. The experiment results show that the features extracted from the layer 49Relu have the best performance for these three datasets.
ORCID iDs
Fei, Zixiang, Yang, Erfu ORCID: https://orcid.org/0000-0003-1813-5950, Li, David ORCID: https://orcid.org/0000-0002-6401-4263, Butler, Stephen ORCID: https://orcid.org/0000-0002-2103-0773, Ijomah, Winifred and Zhou, Huiyu;-
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Item type: Book Section ID code: 72043 Dates: DateEvent11 November 2019Published21 June 2019AcceptedNotes: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Engineering > Design, Manufacture and Engineering Management
Faculty of Science > Strathclyde Institute of Pharmacy and Biomedical Sciences
Faculty of Humanities and Social Sciences (HaSS) > Psychological Sciences and HealthDepositing user: Pure Administrator Date deposited: 16 Apr 2020 08:54 Last modified: 12 Dec 2024 01:23 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/72043