A Gait Recognition Method Based on KFDA and SVM

The algorithm based on KFDA and SVM is proposed. The information of width and angle is extracted from the human motion image sequences. The features of width and angle is merged and reduced by the KPCA. The Low-dimensional gait characteristic is extracted by modified 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 92% and at a relatively lower computational cost.