Fuzzy Principal Component Analysis based Gait Recognition

Gait recognition is a relatively new biometric identification technology for human identification. Gait recognition algorithm based on fuzzy principal component analysis (FPCA) for gait energy image(GEI) is proposed. Firstly, the original gait sequence is preprocessed and gait energy image is obtained. Secondly, the eigenvalues and eigenvectors are extracted by fuzzy principal component analysis, which are called fuzzy components. Then the eigenvectors are projected into lower-dimensional space. Finally, the NN classifier is utilized in feature classification. The method is tested on CASIA database. The experimental results show that this algorithm achieves higher recognition performance. Correct recognition rate (CRR) of 89.7% for FPCA algorithm and 83.1% for GEI algorithm. FPCA algorithm to achieve 100% recognition rate. The two component model, FPCA accounts for 91.7% of the total variance and PCA accounts only for 39.8%.