Title :
Head-pose invariant facial expression recognition using convolutional neural networks
Author_Institution :
IDIAP, Martigny, Switzerland
Abstract :
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.
Keywords :
emotion recognition; feature extraction; neural nets; automatic face analysis; convolutional neural networks; data-driven face analysis approach; feature extraction; head-pose invariant facial expression recognition; lighting variations; multi-scale feature extractors; pose variations; Automatic testing; Character recognition; Convolution; Data analysis; Face recognition; Feature extraction; Neural networks; Neurons; Performance analysis; Robustness;
Conference_Titel :
Multimodal Interfaces, 2002. Proceedings. Fourth IEEE International Conference on
Print_ISBN :
0-7695-1834-6
DOI :
10.1109/ICMI.2002.1167051