A Novel Image Preprocessing by Evolvable Neural Network

Abstract

This paper presents a novel and efficient preprocessing method to relieve the effect of changing illumination by restructuring itself under dynamically changing environments. It monitors situations and evolves its structure accordingly, stores its experiences in the form of artificial chromosomes. It performs adaptive preprocessing by reorganizing its structure using the knowledge in the chromosomes matched to an operation environment. Introducing the concept of combining situation-awareness using the evolvable neural network and the evolutionary computing using the Genetic algorithm, the proposed method not only achieves highly efficient preprocessing for object recognition in varying illumination environments, but also solves the time-consuming problem of the evolutionary computing method. The proposed method has been tested and applied successfully to the preprocessing of face images. Face images are in spacially well-defined object class, and the features of face images are represented by multiple fiducial points, each of which is described by the Gabor wavelet transform. The superiority of the proposed preprocessing method is proven by showing the improvements of object recognition accuracy of face dataset: our lab, the AR, and the Yale.