Feature and model mutual matching face tracking method based on increment principal component analysis

Abstract

The invention discloses a feature and model mutual matching face tracking method based on on-line increment principal component analysis. The method includes the following steps: off-line modeling is performed on a plurality of face images to obtain a model matching (CLM) model A; key point detection is performed on each frame of a face video to be tracked, and a set of all key points and robust descriptors of the key points are combined to form a key point model B; key point matching is performed on each frame of the face video to be tracked on the basis of the key point model B to obtain an initial face gesture parameter set in each frame of the face images; the model A is used for performing CLM face tracking on the face video to be tracked; re-tracking is performed according to the initial face gesture parameter sets and initial tracking results; the model A is updated, the steps are repeated, and final face tracking results are obtained. The feature and model mutual matching face tracking method based on the on-line increment principal component analysis solves the problem of tracking losing occurred when variation between adjacent frames in a target image is large during CLM face tracking, thereby improving tracking accuracy.

Description

technical field [0001] The invention relates to the technical field of computer graphics and images, in particular to a highly robust face tracking method based on online incremental principal component analysis and mutual matching of features and models. Background technique [0002] In recent years, computer vision technology has made great progress, and image recognition and tracking has become a popular research direction in the computer field. Robust real-time face tracking is a core in areas such as intelligent video surveillance and vision-based human-computer interaction and robot navigation. This technology is used in many fields such as video conferencing, public security criminal investigation, access control, financial payment, and medical applications. The human face is a non-rigid recognition object. During the movement, its size and shape will affect the tracking effect. Therefore, real-time face tracking is a challenge to the field of computer vision. [00...

AI Technical Summary

Problems solved by technology

[0007] It can be seen from the above that the current mainstream face tracking technology is still unable to accurately track the face on the premise of ensuring robustness.

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