Head-Pose Invariant Facial Expression Recognition Using Convolutional Neural Networks
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization and initialization procedures. We propose a data-driven face analysis approach that is not only capa...
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| Published in: | Multimodal Interfaces: Proceedings of the International Conference on Multimodal Interfaces (4th: 2002: Pittsburgh, PA) p. 529 |
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| Main Author: | |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Washington, DC, USA
IEEE Computer Society
14.10.2002
IEEE |
| Series: | ACM Conferences |
| Subjects: | |
| ISBN: | 9780769518343, 0769518346 |
| Online Access: | Get full text |
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| Abstract | Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization and initialization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also more robust with regard to face location changes and scale variations when compared to classical methods such as e.g. MLPs. Our approach is based on convolutional neural networks that use multi-scale feature extractors, which allow for improved facial expression recognition results with faces subject to in-plane pose variations. |
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| AbstractList | Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization and initialization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also more robust with regard to face location changes and scale variations when compared to classical methods such as e.g. MLPs. Our approach is based on convolutional neural networks that use multi-scale feature extractors, which allow for improved facial expression recognition results with faces subject to in-plane pose variations. Automatic face analysis has to cope with pose and lighting variations. Pose variations are particularly difficult to tackle and many face analysis methods require the use of sophisticated normalization and initialization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also more robust with regard to face location changes and scale variations when compared to classical methods such as MLPs. Our approach is based on convolutional neural networks that use multi-scale feature extractors, which allow for improved facial expression recognition results with faces subject to in-plane pose variations. |
| Author | Fasel, Beat |
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| Copyright | Copyright (c) 2002 Institute of Electrical and Electronics Engineers, Inc. All rights reserved. |
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| Snippet | Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods... Automatic face analysis has to cope with pose and lighting variations. Pose variations are particularly difficult to tackle and many face analysis methods... |
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| SubjectTerms | Automatic testing Character recognition Computing methodologies Computing methodologies -- Artificial intelligence Computing methodologies -- Artificial intelligence -- Computer vision Computing methodologies -- Artificial intelligence -- Computer vision -- Computer vision problems Computing methodologies -- Artificial intelligence -- Computer vision -- Computer vision problems -- Object recognition Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Machine learning approaches Computing methodologies -- Machine learning -- Machine learning approaches -- Neural networks Convolution Data analysis Face recognition Feature extraction Neural networks Neurons Performance analysis Robustness |
| Title | Head-Pose Invariant Facial Expression Recognition Using Convolutional Neural Networks |
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