A real-time blink detection method based on multi-scale time-series images

Abstract

The invention discloses a real-time blink detection method based on multi-scale time-series images, comprising: positioning the first frame of images in the time-series images to obtain the positions of both eyes; using it to extract human eye images from the first frame of images to obtain human eye Template; use the human eye template to initialize the human eye tracker, use the updated human eye tracker to track the time-series images, obtain the time-series human eye images, and update the human eye tracker to extract the artificial descriptors of the processed time-series human eye images, Then extract the differ feature of the artificial descriptor, and concatenate it with the artificial descriptor to obtain the human eye feature. Encode the human eye features into a blink behavior feature heat map according to the time sequence; then input the blink behavior feature heat map into the LSTM network line by line to obtain multiple hidden states; finally, multiple hidden states are concatenated to obtain multi-scale time series features. Blink detection, to determine whether the time-series images contain blinking behavior. The invention improves the accuracy and stability of blink detection under unconstrained conditions.

Description

technical field [0001] The invention belongs to the technical field of digital image recognition, and more specifically relates to a real-time blink detection method based on multi-scale time-series images. Background technique [0002] With the popularization of various intelligent application devices, it should become a good way of human interaction to reflect the current state of the target by observing the blinking behavior of people, so whether it is living body verification, fatigue driving detection, polygraph detection, etc. There is a great demand. [0003] The current main blink detection algorithms are mainly divided into the following two types: one is to extract the traditional features of a single frame (LBP, HOG, etc.), and then use the classifier (SVM, Adboost, etc.) state; the other is to perform blink detection based on some heuristic rules, such as hough transform to detect pupils, etc. [0004] The above method has the following shortcomings. For the fi...

AI Technical Summary

Problems solved by technology

This solves the technical problems of low accuracy and poor stability in the prior art

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