A study on optimized face recognition algorithm realized with the aid of multi-dimensional data preprocessing technologies and RBFNNs

In this study, we propose the hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we propose hybrid approach based on ASM and the PCA algorithm. In this step, we use a CCD camera to obtain a picture frame. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. ASM(Active Shape Model) to extract the face contour detection and image shape to produce personal profile. The proposed RBFNNs architecture consists of three functional modules such as the condition phase, the conclusion phase, and the inference phase as fuzzy rules for 'If-then' format. In the condition phase of fuzzy rules, input space is partitioned with fuzzy C-means clustering. In the conclusion phase of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution. The proposed RBFNNs are applied to facial recognition system and then demonstrated from the viewpoint of output performance and recognition rate.