Name
Abstract | ||
|---|---|---|
We estimate AU intensity based on mRMR-based feature selection.Adaptive ensemble classifiers are proposed for robust expression classification.The ensemble models show superior ability for novel emotion class detection.Both database images and real human subjects are used to evaluate the system.It outperforms related work reported in the literature for the Bosphorus database. Automatic recognition of facial emotion has been widely studied for various computer vision tasks (e.g. health monitoring, driver state surveillance and personalized learning). Most existing facial emotion recognition systems, however, either have not fully considered subject-independent dynamic features or were limited to 2D models, thus are not robust enough for real-life recognition tasks with subject variation, head movement and illumination change. Moreover, there is also lack of systematic research on effective newly arrived novel emotion class detection. To address these challenges, we present a real-time 3D facial Action Unit (AU) intensity estimation and emotion recognition system. It automatically selects 16 motion-based facial feature sets using minimal-redundancy-maximal-relevance criterion based optimization and estimates the intensities of 16 diagnostic AUs using feedforward Neural Networks and Support Vector Regressors. We also propose a set of six novel adaptive ensemble classifiers for robust classification of the six basic emotions and the detection of newly arrived unseen novel emotion classes (emotions that are not included in the training set). A distance-based clustering and uncertainty measures of the base classifiers within each ensemble model are used to inform the novel class detection. Evaluated with the Bosphorus 3D database, the system has achieved the best performance of 0.071 overall Mean Squared Error (MSE) for AU intensity estimation using Support Vector Regressors, and 92.2% average accuracy for the recognition of the six basic emotions using the proposed ensemble classifiers. In comparison with other related work, our research outperforms other state-of-the-art research on 3D facial emotion recognition for the Bosphorus database. Moreover, in on-line real-time evaluation with real human subjects, the proposed system also shows superior real-time performance with 84% recognition accuracy and great flexibility and adaptation for newly arrived novel (e.g. 'contempt' which is not included in the six basic emotions) emotion detection. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.eswa.2014.08.042 | Expert Syst. Appl. |
Keywords | Field | DocType |
support vector regression | Computer science,Emotion recognition,Emotion classification,Mean squared error,Personalized learning,Artificial intelligence,Cluster analysis,Feedforward neural network,Ensemble forecasting,Pattern recognition,Support vector machine,Speech recognition,Machine learning | Journal |
Volume | Issue | ISSN |
42 | 3 | 0957-4174 |
Citations | PageRank | References |
20 | 0.69 | 57 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Zhang | 1 | 20 | 0.69 |
| Li Zhang | 2 | 20 | 0.69 |
| M. Alamgir Hossain | 3 | 107 | 16.52 |