Image Chaotic Characteristics and Application in Face Recognition

To explore the widely possibilities of chaos theory in image application, this paper investigates a new image feature construction method based on chaos iteration to reduce the impact of local dynamic changes in the image recognition. The discrete dynamical system is constructed by the image and the auxiliary function. And the approximate attractors which are considered as the image feature lattice got from the discrete dynamical system through Euler iteration. The paper achieves the face recognition by calculating the correlation coefficient of the feature lattice which is projected into the one-dimensional space according to the Radon transform. A gray-adaptive method is proposed to improve the quality of the attractors by adjusting the gray contrast. Experiments show that with the proposed improved gray adaptive method, the identification rate of single feature in the Yalefaces rises from 70.91% to 87.33%. In addition, the experiment indicates that the shapes of the three-dimensional attractors vary with the image sequence.