Human eye model training method, human eye identification method, apparatus and device, and medium

Abstract

Disclosed are a human eye model training method, a human eye identification method, apparatus and device, and a medium. The method comprises: acquiring a face image sample and marking the face image sample to obtain face image sample data, extracting a feature vector of the face image sample, and dividing the face image sample data into training sample data and verification sample data; training asupport vector machine classifier through 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 according to the vector distance and the labeling data corresponding to the verification sample, and obtaining a human eye judgment model according to the classification threshold. Through the training method of the human eye model, a human eye judgment model with high accuracy of judging whether the human eye 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 human eye model training method, a human eye recognition method, a device, equipment and a medium. Background technique [0002] With the rapid development of artificial intelligence, human eye positioning recognition has received extensive 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, unoccluded eye images are needed, but the eye images recognized by the conventional face feature point recognition algorithm cannot screen ...

AI Technical Summary

Problems solved by technology

However, in some image processing processes, unoccluded eye images are needed, but the eye images recognized by the conventional face feature point recognition algorithm cannot screen the occluded images, which is easy to introduce errors, which is not conducive to subsequent further research. deal with needs

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