Gait Analysis and Recognition Using Multi-Views Gait Database
This paper presents an automatic gait recognition system that recognizes a person by the way he/she walks. The gait signature is obtained based on the contour width information of the silhouette. Using this statistical shape information, the authors could capture the compact structural and dynamic features of the walking pattern. As the extracted contour width feature is large in size, Fisher Discriminant Analysis is used to reduce the dimension of the feature set. After that, a modified Probabilistic Neural Networks is deployed to classify the reduced feature set. Satisfactory result could be achieved when they fuse gait images from multiple viewing angles.