Face key point detection model training method and face key point detection method

Abstract

The invention discloses a face key point detection model training method and a face key point detection method. The training method comprises the following steps: acquiring a face sample image carrying preset face labeling information; inputting the face sample image into a Pnet network of an MTCNN network to obtain a face candidate box corresponding to the face sample image; inputting the face candidate frame and the face sample image into an Rnet network of the MTCNN network to obtain a face target frame corresponding to the face sample image; and inputting the face target frame, the preset face labeling information and the face sample image into an Onet network of the MTCNN network to obtain a trained MTCNN network model, and a key point regression function in the Onet network is a preset WingLoss function, when an existing face key point detection method is used for face detection, the problems of key point drifting is easy to occur, so that the face key point detection accuracy is relatively low are solved.

Description

Technical field [0001] The present application relates to the field of computer visual, and in particular, there is a training method of face-critical point detection model and a key point detection method. Background technique [0002] Face-faced point detection has a key role in numerous applications, such as face posture correction, posture identification, expression identification, fatigue monitoring, mouth identification, etc. Therefore, how to get high-precision face key has always been a research hotspot in computer visual field. [0003] Face-faced point detection refers to a given face image, which is positioned in a key point of the face, such as eyebrows, eyes, nose, mouth, face profile. Existing face-key detection methods When performing face detection, it is prone to critical displacement, causing a lower key point detection accuracy. Inventive content [0004] The present application provides a training method of a face-to-face detection model and a key point detec...

AI Technical Summary

Problems solved by technology

[0004] This application provides a training method for a human face key point detection model and a human face key point detection method, which solves the problem that the existing key point detection method of human face is prone to key point drift when performing face detection, resulting in Technical issues with low keypoint detection accuracy

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