Abstract: In the research of computer vision, the recognition of image objects based on video data is
on an increasing trend. Focusing on the feature extraction of video data, a method based on
trigonometric function iteration was proposed to extract 3D iterative trajectory features of the video.
Considering the time and space dimensions of video data, this paper constructed a three-dimensional
dynamic system by using a trigonometric function, obtained the features of video segment data as a
whole in one extraction, and extracted a set of three-dimensional feature points similar to chaotic
attractors. This iterative feature of video data is an iterative set of track points. Face recognition
experiments using VidTIMIT datasets of face videos show that increasing the number of initial
iterations and reducing the number of iterations could lead to a better effect of the extracted feature
points set. After 43 groups of 559 videos of VidTIMIT were all experimented with, the recognition
rate could reach 88.16%. Compared with other methods recorded in the existing literature, the method
proposed in this paper is characterized by high recognition rate and short computing time. It is proved
that this 3D video iterative trajectory feature is of great practical significance and requires further
research, analysis and verification.

Key words: dynamic system, iteration, video, face recognition