Cascade regression-based face key point positioning method

Abstract

The invention relates to a cascade regression-based face key point positioning method. The cascade regression-based face key point positioning method includes the following steps that: 1) a large number of face image data are acquired, and an initial key point position is marked; 2) the face image data are trained, and a coarse regression machine can be obtained through learning, and then with the output of the coarse regression machine adopted as input, a fine regression machine can be obtained through learning; and 3) face image data to be recognized are given, and the initial shape of a face is regressed to the vicinity of a real shape through the coarse regression machine, and then, with the output of the coarse regression machine adopted as input, the precise coordinates of a face key point can be obtained through the fine regression machine. According to the cascade regression-based face key points positioning method of the invention, a coarse-to-fine cascade regression algorithm is adopted, and a large number of samples are learned, and multi-feature fusion and multiple-regression machine fusion are realized, and therefore, the speed and the robustness of the algorithm are improved greatly, and the face key point can be excellently positioned under the situations of occlusion, low lightness and poses such as side faces, and the accuracy and the speed of face key point positioning can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of digital image processing and face recognition, and in particular relates to a method for locating key points of a face based on cascade regression. Background technique [0002] The key points of the face are some key points of the face with strong representation ability, such as eyes, nose, mouth and facial contour. Key point positioning plays an important role in the field of face recognition. For example, face recognition, tracking, expression analysis and 3D modeling all rely on the results of key point positioning. [0003] The traditional face key point location method is based on the parametric shape model method. According to the apparent features near the key point, a parameter model is learned, and the position of the key point is iteratively optimized during use, and finally the key point coordinates are obtained. [0004] The above-mentioned face key point positioning methods are all heavily ...

AI Technical Summary

Problems solved by technology

Accurate results cannot be obtained when occlusion, illumination and pose changes are large

At the same time, due to the rapid development of mobile terminal demand, the above method cannot realize real-time processing through mobile terminals such as mobile phones.

Images