Eye key point labeling method and device and training method and device for eye key point detection model

Abstract

The embodiment of the invention discloses an eye key point labeling method and device and a training method and device for an eye key point detection model. The eye key point labeling method comprisesthe steps of obtaining a face image and a marked eyelid curve corresponding to the face image; for each marked eyelid curve, based on a mathematical integration principle, integrating the marked eyelid curves, and determining a first curve length of a labelled upper eyelid curve and a second curve length of a labelled lower eyelid curve in the labelled eyelid curves; determining a plurality of eyelid points to be utilized from the labeled upper eyelid curve and the labelled lower eyelid curve; based on the first curve length of the labelled upper eyelid curve, the second curve length of the labelled lower eyelid curve, the eyelid point to be utilized and a preset equal division point number, determining the equally-divided upper eyelid points and the equally-divided lower eyelid points from the labelled upper eyelid curve and the labelled lower eyelid curve respectively, and then determining the eye key points corresponding to each face image by combining the labelled eyelid points. The eye key points with obvious semantic features are labelled, and the marking efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent detection, in particular to a method and device for marking eye key points and training a detection model thereof. Background technique [0002] The detection of eye key points in the image plays an important role in many aspects. For example, in the process of fatigue detection, the fatigue detection system can detect the eyes contained in the image based on the pre-trained eye key point detection model. The eye key points of the upper and lower eyelids of the human eye, and then, based on the eye key points of the upper and lower eyelids, the distance between the upper and lower eyelids of the human eye is calculated as the opening and closing distance of the human eye, and then based on the opening and closing distance of the human eye in the predicted Set the opening and closing distance within the duration, determine whether the person corresponding to the image is in a fatigue state, and...

AI Technical Summary

Problems solved by technology

In related technologies, the above-mentioned eye key points are generally manually marked by annotators. For the eye key points manually marked by annotators, different annotators have different standards for marking eye key points. The semantic features of eye key points are not obvious, and the efficiency of manually labeling eye key points is low

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