Abstract An improved gait recognition approach based on ground reaction force (GRF) is proposed. 3directional GRF are acquired by 3dimensional force plate while a person is walking through the gait walkway. Wavelet packet (WP) decomposition is used to extract features in timefrequency domain, and optimal feature subset is selected using a fuzzy cmeans (FCM) clustering algorithm. Support vector machine (SVM) classifier is trained on trainingset, and then gait recognition is implemented by SVM on testing set. In order to improve the recognition accuracy, waveform alignment and resampling approach are utilized. Multiple classifiers are designed to reduce the negative influence of changes in walking speed. The approach is tested on a gait database collected from 103 subjects. Comparative results demonstrate that high recognition accuracy can be reached even in fewer trainingsamples.
2011, Vol. 24 
