Audio-visual emotion recognition in adult attachment interview
Proceedings of the 8th international conference on Multimodal interfaces
A Fuzzy One Class Classifier for Multi Layer Model
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
A one class KNN for signal identification: a biological case study
International Journal of Knowledge Engineering and Soft Data Paradigms
Automatic temporal segment detection and affect recognition from face and body display
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Refining image retrieval using one-class classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Audio-visual spontaneous emotion recognition
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
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In this paper, we explore one-class classification application in recognizing emotional and nonemotional facial expressions occurred in a realistic human conversation setting--Adult Attachment Interview (AAI). Although emotional facial expressions are defined in terms of facial action units in the psychological study, non-emotional facial expressions have not distinct description. It is difficult and expensive to model non-emotional facial expressions. Thus, we treat this facial expression recognition as a one-class classification problem which is to describe target objects (i.e. emotional facial expressions) and distinguish them from outliers (i.e. non-emotional ones). We first apply Kernel whitening to map the emotional data in a kernel subspace with unit variances in all directions. Then, we use Support Vector Data Description (SVDD) for the classification which is to directly fit a boundary with minimal volume around the target data. We present our preliminary experiments on the AAI data, and compare Kernel whitening SVDD with PCA+SVDD and PCA+Gaussian methods.