Real-time face key point positioning method based on Android platform

Abstract

The invention discloses a real-time face key point positioning method based on an Android platform, and belongs to the field of computer vision technology. The method disclosed by the invention comprises the following steps: collecting a face training image set, and calibrating key points; randomly selecting n samples in the training set to serve as an initial shape of each training shape; calculating a standard target of each training shape; extracting a shape index feature of each key point; using a correlation analysis method to select proper features; using a two-layer enhanced regression structure (an external layer and an internal layer); calculating the regression device of each stage; and using a face detection method to estimate a face window, and predicating a face key point position according to a trained regression model. The existing methods are higher in computational complexity and excessively slow in operation on a mobile platform; and moreover, the existing methods are sensitive to noise and are lower in positioning precision. According to the real-time face key point positioning method disclosed by the invention, the shape is constrained by linear combination of samples, and the method based on regression is used for improving the face key point positioning precision and efficiency.

Description

technical field [0001] The invention relates to the technical field of computer vision, and relates to a method for locating key points of a human face. Background technique [0002] As the key technology of computer vision research, face detection and key point positioning technology have been widely used in intelligent monitoring, identity recognition, expression analysis and other aspects. Face key point positioning refers to the precise positioning of specific facial organs such as eyes, mouth, and nose in the face image, and obtaining their geometric parameters, so as to provide accurate information for research such as expression analysis or face recognition. [0003] At present, the location of face key points has been well realized on the computer, and the real-time and accuracy are very high. Representative works include Active Shape Model (ActiveShapeModel, ASM), Bayesian Tangent Shape Model (BayesianTangentShapeModel , BTSM), active appearance model (ActiveAppear...

AI Technical Summary

Problems solved by technology

However, the existing facial feature point positioning and tracking algorithms generally have high computational complexity, large memory consumption, and slow processing speed, so it is difficult to directly transplant them to mobile platforms.

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