Face Recognition Using DCT and Hybrid Flexible Neural Tree

This paper proposes a new face recognition approach by using the Discrete Cosine Transform (DCT) and hybrid flexible neural tree (FNT) classification model. The DCT is employed to extract the input features to build a face recognition system, and the flexible neural tree is used to identify the faces. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This framework allows input features selection, over-layer connections and different activation functions for the various nodes involved. The FNT structure is developed using an evolutionary algorithm and the parameters are optimized by a particle swarm optimization algorithm. Empirical results indicate that the proposed framework is efficient for face recognition.