2 2 NIGHT VISION FACE RECOGNITION METHOD USING 2-Directional 2-Dimensional Principal Component Analysis ALGORITHM AND Polynomial-based Radial Basis Function Neural Networks

摘要

The present invention relates to a method of recognizing a face in an environment without illumination, comprising the steps of: (1) acquiring an image using a night vision camera; (2) a data preprocessing step of detecting a face region in the image data obtained in the step (1) and removing a disturbance value; (3) The data preprocessed in the above step (2) is subjected to a two-dimensional 2D principal component analysis (hereinafter referred to as '2D (2D) PCA' Reducing the data dimension while using both directions intact; (4) Polynomial-based Radial Basis Function Neural Networks (hereinafter referred to as " pRBFNNs ") to recognize faces in the image data with reduced data dimensions And is characterized by its constitution. According to the two-way two-dimensional principal component analysis algorithm proposed by the present invention and the nighttime facial recognition method using the optimal polynomial radial basis function based neural network, it is possible to recognize the face even in the environment without illumination by acquiring the image through the night vision camera Do. Further, according to the present invention, the face area is detected in the obtained image data, the disturbance value is removed, and then the two-dimensional two-dimensional principal component analysis algorithm is used to reduce the data dimension while using the two directions of the two- And the recognition rate can be improved. In addition, according to the present invention, a polynomial radial basis function based neural network is used, but a fuzzy C-means (FCM) clustering algorithm is used, and the degree of polynomial, the number of clusters, By optimizing, the linear decision boundary in the output space can be represented by the nonlinear decision boundary, and the recognition performance is improved by fast learning convergence in the optimized parameter.