A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics
- 1 September 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops
Abstract
This study investigates recognition of affect in human walking as daily motion, in order to provide a means for affect recognition at distance. For this purpose, a data base of affective gait patterns from non-professional actors has been recorded with optical motion tracking. Principal component analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) are applied to kinematic parameters and compared for feature extraction. LDA in combination with naive Bayes leads to an accuracy of 91% for person-dependent recognition of four discrete affective states based on observation of barely a single stride. Extra-success comparing to inter-individual recognition is twice as much. Furthermore, affective states which differ in arousal or dominance are better recognizable in walking. Though primary task of gait is locomotion, cues about a walker's affective state are recognizable with techniques from machine learning.Keywords
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