Nonlinear dynamical system iteration applied in video face feature extraction and recognition

Video recognition is the fundamental way human eyes and brains perceive the world. The human brain's real recognition, thinking, and memory mechanism has nonlinear dynamical system models, and the interaction between neurons exists in chaotic iterative patterns characterized by nonlinear dynamics. This paper proposes a new method for extracting video face features based on a nonlinear dynamical system, which is constructed by using video images as ternary functions and Discrete Cosine Transform (DCT) basis functions as auxiliary functions. Then, chaotic iterations are performed on the system, and the iteration trajectories are used as video features. Finally, the Pearson correlation coefficient between features is calculated to determine whether the faces in different videos are similar to complete the recognition work. Regarding database preprocessing, we propose Adaptive Threshold Region Detection (ATRD) algorithm for video region segmentation to optimize the database and improve recognition performance. Overall, this paper's method is simple, flexible, and has high recognition accuracy, with good recognition results on the YouTube and VidTIMIT databases. Of course, as a new method, there is still room for improvement and refinement.