Face tracking method based on multiple appearance models

Abstract

The invention provides a face tracking method based on a multiple appearance models. The method comprises the steps of face tracker start-up, tracking detection, multiple appearance model building, appearance similarity score calculation and face location based on weighted fractional level fusion. During the process, a variety of appearance models are used for matching in different tracking scenarios; a long-term facial appearance feature update relationship diagram is used for learning; and sub-images of the same structure are used for face location. The invention provides a face tracker which uses multiple appearance models for face tracking, wherein a face detector assists the face tracker in face location during short-term appearance changes and reinitialization. A face position with the highest fusion score is selected as a candidate, and the final tracking output is acquired. The problems of obstacle obstruction, scale change and the like in the tracking process are effectively solved. The face tracking method makes further contributions to new design in engineering field and innovative solutions in monitoring field.

Description

Technical field [0001] The present invention relates to the field of face tracking, in particular to a method for face tracking based on multiple appearance models. Background technique [0002] Face tracking detection technology is one of the research hotspots in the field of computer vision and pattern recognition. Face tracking is the process of determining the movement trajectory and size change of a face in a video or image sequence. It is the first step in dynamic face information processing, which involves pattern recognition, image processing, computer vision, physiology, Many disciplines such as psychology and morphology are closely related to the research fields of identification methods based on other biological characteristics and computer human-computer perception interaction. There are important applications in video conferences, video phones, video surveillance, and intelligent buildings. As a key technology in the fields of automatic face recognition, video conf...

AI Technical Summary

Problems solved by technology

[0003] Since the face itself is a complex and changeable non-rigid body model, its size and shape will change during the movement process, and the existing methods cannot completely solve many problems such as motion blur, fast movement, obstacle occlusion, background clutter and scale changes. Tracking problems, so it is challenging to implement real-time and reliable face tracking methods

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