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Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
| Content Provider | MDPI |
|---|---|
| Author | Kim, Jin-Chul Kim, Min-Hyun Suh, Han-Enul Naseem, Muhammad Tahir Lee, Chan-Su |
| Copyright Year | 2022 |
| Description | Facial expression recognition is very useful for effective human–computer interaction, robot interfaces, and emotion-aware smart agent systems. This paper presents a new framework for facial expression recognition by using a hybrid model: a combination of convolutional neural networks (CNNs) and a support vector machine (SVM) classifier using dynamic facial expression data. In order to extract facial motion characteristics, dense facial motion flows and geometry landmark flows of facial expression sequences were used as inputs to the CNN and SVM classifier, respectively. CNN architectures for facial expression recognition from dense facial motion flows were proposed. The optimal weighting combination of the hybrid classifiers provides better facial expression recognition results than individual classifiers. The system has successfully classified seven facial expressions signalling anger, contempt, disgust, fear, happiness, sadness and surprise classes for the CK+ database, and facial expressions of anger, disgust, fear, happiness, sadness and surprise for the BU4D database. The recognition performance of the proposed system is 99.69% for the CK+ database and 94.69% for the BU4D database. The proposed method shows state-of-the-art results for the CK+ database and is proven to be effective for the BU4D database when compared with the previous schemes. |
| Starting Page | 5493 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app12115493 |
| Journal | Applied Sciences |
| Issue Number | 11 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-28 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Facial Expression Recognition Convolutional Neural Networks Support Vector Machine Mixture of Classifiers Hybrid Model |
| Content Type | Text |
| Resource Type | Article |
