Multi-pose eye positioning algorithm based on cascaded convolutional neural network

A convolutional neural network and eye positioning technology, applied in the fields of machine learning and computer vision, can solve problems such as the influence of eye and head posture changes, and achieve good results

Inactive Publication Date: 2018-03-02
SOUTH CHINA UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that traditional eye positioning is easily affected by head posture changes

Method used

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  • Multi-pose eye positioning algorithm based on cascaded convolutional neural network
  • Multi-pose eye positioning algorithm based on cascaded convolutional neural network
  • Multi-pose eye positioning algorithm based on cascaded convolutional neural network

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Embodiment Construction

[0021] The specific embodiments of the present invention will be further described below in conjunction with the drawings.

[0022] In this embodiment, the proposed multi-posture eye positioning method based on cascaded convolutional neural network can overcome the problem of decreased eye positioning accuracy caused by head deflection. Such as Figure 1a Establish a multi-task cascaded convolutional neural network model: collect face images and preprocess the face images to obtain the annotation data corresponding to different tasks to form a data set; construct a multi-task cascaded convolutional neural network ; Input the obtained training data set into the network and use the fast training method to obtain the network model; first, the input image is subjected to pyramid scale transformation, and the obtained model and the improved non-maximum suppression algorithm are used for prediction to achieve multi-pose Eye positioning.

[0023] In this embodiment, in the training phase,...

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Abstract

The invention discloses a multi-pose eye positioning algorithm based on the cascaded convolutional neural network, belongs to the machine learning and computer vision field and is suitable for intelligent systems such as face recognition, sight tracking and driver fatigue detection. The method comprises steps that face pictures marked with various types of information are collected to form a training data set; the multi-task cascaded convolutional neural network is constructed; the training data set is utilized to train the network to acquire a network model; and lastly, the network model is utilized to detect faces and face key points of the pictures, and the smallest rectangular box containing the eye key points is selected as the eye positioning result. The method is advantaged in thatthe multi-task cascading convolutional neural network is utilized to accomplish face detection and face key point detection, so the multi-pose eye positioning effect is obviously improved.

Description

Technical field [0001] The invention belongs to the field of machine learning and computer vision, and specifically is a multi-posture eye positioning method based on cascaded convolutional neural networks. Background technique [0002] Face images contain a wealth of information. The research and analysis of face images is an important direction and research hotspot in the field of computer vision. The eyes are the most important human senses, which contain unique biological characteristics and rich emotional information. Through the analysis of the eyes, we can understand human emotions and behaviors. For example, in human-computer interaction, the sight of the eyes can realize non- Contact interaction, in terms of fatigue driving detection, judges whether the driver is fatigued by the state of the eyes and so on. [0003] In the past ten years, a large number of scholars have conducted research on eye positioning. Generally speaking, eye positioning algorithms are mainly divide...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/66G06N3/08
CPCG06N3/08G06V40/165G06V40/171G06V40/18G06V30/194
Inventor 秦华标刘青
Owner SOUTH CHINA UNIV OF TECH
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