Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Facial expression recognition using a dynamic model and motion energy
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Face image analysis by unsupervised learning and redundancy reduction
Face image analysis by unsupervised learning and redundancy reduction
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
A facial expression recognition system based on supervised locally linear embedding
Pattern Recognition Letters
Recognition of Patterns Without Feature Extraction by GRNN
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Multilevel image segmentation with adaptive image context based thresholding
Applied Soft Computing
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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.