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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Veröffentlicht in:Multimodal Interfaces: Proceedings of the International Conference on Multimodal Interfaces (4th: 2002: Pittsburgh, PA) S. 529
1. Verfasser: Fasel, Beat
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 14.10.2002
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
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
ISBN:9780769518343, 0769518346
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9780769518343
0769518346
DOI:10.1109/ICMI.2002.1167051