Human face critical organ contour characteristic points positioning and tracking method and device

Abstract

The invention discloses a method for positioning and tracking outer contour feature points of key face organs and a device thereof, comprising the following steps: first, the positions of canthi points and angulus oris points on the face are obtained; the medial axis of the eyes and the mouth is identified based on the positions; the outer contour feature points of the eyes and eyebrows are identified on the medial axis of the eyes, and the outer contour feature points of the mouth are identified on the medial axis of the mouth. Consistent positioning and tracking of outer contour feature points of key face organs are conducted on such a basis. The technical proposal provided by the embodiment of the invention solves the problem of inaccurate positioning of outer contour feature points of eyes, mouth and eyebrows when people makes various faces; based on the positioning of the feature points, the existing two-dimensional and three-dimensional face models can be real-time driven, and face expressions and motions such as frowning, blinking and mouth opening of people before a camera can be real-time simulated, thus creating various real and vivid face animations.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a method and device for locating and tracking feature points on the outer contours of key organs of a human face. Background technique [0002] At present, in the face detection and localization technology, it can be roughly divided into organ-based detection and localization methods and template-based detection and localization methods. The organ-based method regards the human face as a combination of salient organs, first extracts some important organs: eyes, nose, lips, etc., and then detects the face through the position of the organs and the geometric relationship between them. The performance of face key organ localization largely affects the accuracy of face detection methods. [0003] Traditional methods based on Active Appearance Models (AAM, Active Appearance Models) and Active Shape Models (ASM, Active Shape Models) generally use training models to l...

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Problems solved by technology

[0005] However, the method in the above-mentioned patent application cannot accurately locate the outer contours of the eyes, eyebrows and mouth of the human face, nor can it accurately track the outer contour points of these key organs of the human face. Therefore, applying it to two-dimensional and three-dimensional human There are certain limitations in face pose estimation and face animation synthesis

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