Machine Learning
How good are support vector machines?
Neural Networks
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
IEEE Transactions on Information Technology in Biomedicine
Automatic gait recognition via statistical approaches for extendedtemplate features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Integrating Face and Gait for Human Recognition at a Distance in Video
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gait Recognition Using Radon Transform and Linear Discriminant Analysis
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Kernel Component Analysis Using an Epsilon-Insensitive Robust Loss Function
IEEE Transactions on Neural Networks
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Gait recognition is a relatively new subfield in biometric recognition, which attempts to recognize people from the way they walk or run. This paper discusses silhouette-based feature descriptor. Human silhouette geometry is generated by boundary tracking approach and resampled to a normalized format. Boundary-centroid distance is proposed to describe gait modality. Then, we apply wavelet transform to boundary-centroid distance, and extract wavelet descriptor. At the same time, we obtain the human skeleton model and extract body's dynamic parameters to express gait modality. We carry out human identification based on SVM using the two kinds of gait feature. The performances based on the two features are compared. Multiple feature fusion and multiple views fusion are carried out and the recognition results demonstrate that the performance of multiple features and multiple views recognition is better than any single feature and single view recognition.