Regression-based active appearance model initialization method

Abstract

The invention discloses a regression-based active appearance model initialization method, and belongs to the computer vision field. The realization process of the method is as follows: when human face characteristic point automatic tracking is carried out by use of an active appearance model, assuming object position information of a first frame in known video tracking, during a subsequent tracking process, by use of a double-threshold characteristic corresponding algorithm, obtaining corresponding discrete characteristics between neighboring frame images, and by use of a space mapping relation between the discrete characteristic points and structured calibration points, established through a nucleus ridge regression algorithm, obtaining initial calibration of human face characteristics, such that the subsequent iteration frequency can be substantially reduced, and at the same time, the calibration precision is improved. Compared to an initialization method of a conventional active appearance model, more accurate human face characteristic point calibration results can be obtained by use of the auxiliary active appearance model.

Description

technical field [0001] The invention belongs to the technical field of image analysis, in particular to a regression-based active appearance model initialization method. Background technique [0002] In the field of computer vision research, the use of Active Appearance Model (Active Appearance Model, AAM) method to locate target shape feature points is a hot spot of attention and research in recent years. It was first proposed by Edwards et al. It has been widely used in the registration and recognition of other non-rigid bodies. The AAM algorithm is an improvement to the Active Shape Model (ASM). Compared with ASM, it considers the constraints of global information and adopts the statistical constraints of shape and texture fusion, that is, the statistical appearance constraints. Moreover, the search principle of AAM draws on the main idea of ​​analysis-by-synthesis (ABS), and makes the model gradually approach the actual input model by continuously adjusting the paramete...

AI Technical Summary

Problems solved by technology

The traditional AAM initialization methods include: (1) using the average face as the initial shape, and the initialization error is large; (2) using face features (such as eyes, mouth) positioning information to complete the initialization, but there are high requirements for positioning accuracy; (3) In video tracking, the positioning result of the previous frame is used as the initial information of the current frame positioning, but it can only adapt to the situation where the change between frames is small

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