Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modern Information Retrieval
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Spontaneous vs. posed facial behavior: automatic analysis of brow actions
Proceedings of the 8th international conference on Multimodal interfaces
A survey of affect recognition methods: audio, visual and spontaneous expressions
Proceedings of the 9th international conference on Multimodal interfaces
Emotionally aware automated portrait painting
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates
International Journal of Computer Vision
Probabilistic semantic classifier
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Face Image Annotation in Impressive Words by Integrating Latent Semantic Spaces and Rules
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Towards multimodal emotion recognition: a new approach
Proceedings of the ACM International Conference on Image and Video Retrieval
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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For supervised training of automatic facial expression recognition systems, adequate ground truth labels that describe relevant facial expression categories are necessary. One possibility is to label facial expressions into emotion categories. Another approach is to label facial expressions independently from any interpretation attempts. This can be achieved via the facial action coding system (FACS). In this paper we present a novel approach that allows to automatically cluster FACScodes into meaningful categories. Our approach exploits the fact that FACScodes can be seen as documents containing terms -the action units (AUs) present in the codes-and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. The FACScode derived descriptions are computed by Latent Semantic Analysis (LSA) and Probabilistic Latent Semantic Analysis (PLSA). We show that, as a high-level description of facial actions, the newly derived codes constitute a competitive alternative to both basic emotion and FACScodes. We have used them to train different types of artificial neural networks