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

A face key point and positioning method technology, applied in the field of face key point positioning, can solve problems such as high computational complexity, large memory consumption, and slow processing speed

Active Publication Date: 2016-01-06
NANJING UNIV OF INFORMATION SCI & TECH
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  • Application Information

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.

Method used

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

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Embodiment Construction

[0023] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings. The invention discloses a real-time face key point positioning method based on an Android platform.

[0024] Such as figure 1 As shown, the present invention can be divided into two stages of learning positioning and testing positioning, specifically, comprising the following steps:

[0025] Step 1, labeling and standardization of training set feature points: the face calibration points involved in the present invention are selected according to the MPEG-4 standard, because the 84 feature points defined by the face definition parameter FDP are too complicated, so we selected them as needed Of the 68 feature points, only eight feature points on the eye contour (that is, four points on the upper, lower, left, and right sides of the eyes) are reserved to improve computing efficiency. The training library is taken from MUCT and self-calibration ...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V40/171G06V40/161
Inventor 刘青山王东杨静邓健康
Owner NANJING UNIV OF INFORMATION SCI & TECH
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