Abstract
The algorithm based on KPDA and SVM is proposed. Firstly, gait energy image (GEI) and moment gait energy images (MGEI) are combined for expressing objects and features reduction. Then the low-dimensional gait characteristic is extracted by KFDA, which can obtain the best projection direction and enhance the capacity of data classification. Then the support vector machine (SVM) models are trained by the decomposed feature vectors. The gaits are classified by the trained SVM models. This algorithm is applied to a data-set including thirty individuals. Extensive experimental results demonstrate that the proposed algorithm performs at an encouraging recognition rate of 91% and at a relatively lower computational cost.
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