Kazunori Kotani

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Permutation ambiguity of the classical Independent Component Analysis (ICA) may cause problems in feature extraction for pattern classification. Especially when only a small subset of components is derived from data, these components may not be most distinctive for classification, because ICA is an unsupervised method. We include a selective prior for(More)
Emergence of novel techniques, such as the invention of MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach in this paper to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a series of unit actions, each of(More)
This paper describes a new method of facial expression recognition based on independent component analysis (ICA) and eigen-space method. We had proposed eigen-space method based on class-features (EMC), and EMC was the outstanding method with classification accuracy superior to multiple discriminant analysis (MDA). Our new method, GEMC, is a generalization(More)