A Two-fold PCA-Approach for Inter-Individual Recognition of Emotions in Natural Walking

This paper describes recognition of emotions of an unkown person during natural walking. As gait data is redundant, high dimensional and variable, effective feature extraction is essential. A combination of two consecutive Principal Component Analyses (PCA) and Fourier Transformation is used for data reduction. Naive Bayes, 1-Nearest Neighbor and Support Vector Machine (SVM) are compared for classification. Best accuracy is achieved for Naive Bayes with 72% for the four

• Type: Conference
• Year: 2009
• Where: MLDM
• Authors: Michelle Karg, Robert Jenke, Kolja Kühnlenz, Martin Buss
• Keywords: Consecutive Principal Component, Machine Learning, MLDM 2009, Naive Bayes, Paper Describes Recognition