Face recognition using Kernel PCA and hybrid flexible neural tree

This paper proposes a new face recognition approach by using Kernel Principal Component Analysis (KPCA) and hybrid Flexible Neural Tree (FNT) classification model. To improve the quality of the face images, a series of image pre-processing techniques, which include histogram equalization, edge detection and geometrical transformation etc. The KPCA is employed to extract features for reducing the dimension of the face pattern, and the Hybrid FNTs are used to identify the faces. To accelerate the convergence of the FNT and improve the quality of the solutions, the Extended Compact Genetic Programming (ECGP) and Particle Swarm Optimization (PSO) are applied to optimize the FNT structure and parameters. The experimental results show that the proposed framework is efficient for face recognition.