Mouth model training method, mouth recognition method, device and apparatus and medium

Abstract

The invention discloses a mouth model training method, a mouth recognition method, device and apparatus and a medium. The method includes obtaining a face image sample, marking the face image sample to obtain face image sample data, extracting a feature vector of the face image sample, dividing the face image sample data into training sample data and verification sample data; training a support vector machine classifier by the training sample data, and obtaining the critical surface of the support vector machine classifier; calculating the distance between the eigenvector of the verification sample and the vector of the critical surface in the verification sample data; obtaining a preset real class rate or a preset pseudo-positive class rate, obtaining a classification threshold accordingto the vector distance and the labeling data corresponding to the verification sample data, and obtaining a mouth judgment model according to the classification threshold. Through the mouth model training method, a mouth judgment model with high accuracy of judging whether the mouth is occluded or not can be obtained.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a mouth model training method, mouth recognition method, device, equipment and medium. Background technique [0002] With the rapid development of artificial intelligence, facial facial features positioning recognition has received widespread attention and has become a hot topic in the field of artificial intelligence. Traditionally, in the existing facial feature point recognition algorithm, the positions of different organs, such as eyes, ears, mouth or nose, can be marked from the face picture, even if the corresponding parts are blocked (glasses, hair, covering mouth, etc.), the algorithm can still identify the relative positions of different parts and provide corresponding pictures. However, in some image processing processes, what is needed is an unoccluded mouth image, and the mouth image recognized by the conventional facial feature point recognition algorithm ...

AI Technical Summary

Problems solved by technology

However, in some image processing processes, what is needed is an unoccluded mouth image, and the mouth image recognized by the conventional face feature point recognition algorithm cannot filter the occluded image, which is easy to introduce errors, which is not conducive to subsequent further research. deal with needs

Images