Face detection method based on multi-scale cascade densely connected neural network

Abstract

The invention discloses a face detection method based on a multi-scale cascade dense connection neural network, belongs to the fields of image processing and computer vision, and is applied to intelligent systems of face recognition, face expression recognition, driver fatigue detection and the like. The face detection method comprises a construction method of a region proposal network and a construction method of a multi-stage densely connected convolutional network model and particularly comprises the steps that face pictures annotated with face bounding box information are collected to forma training data set meeting the input conditions of various sub-networks; the cascade densely connected neural network with the high generalization capacity is constructed; the various sub-networks are trained by means of the training data set separately, and an overall network model is obtained; and finally multi-pose faces in the pictures are detected by means of the overall network model. According to the face detection method, by introducing the dense connection mode into the network, the network can fully extract face feature information, and then the accurate rate of face detection in multiple poses is increased.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, in particular to a face detection method based on a multi-scale cascaded densely connected neural network. Background technique [0002] Face images contain a wealth of information, and the research and analysis of face images is an important direction and research hotspot in the field of computer vision. For example, in various artificial intelligence applications such as face recognition, crowd monitoring, photography, human-computer interaction, and fatigue driving, face detection is the key first step in these technologies. will be valuable. [0003] In the past ten years, a large number of scholars have conducted in-depth research on multi-pose face detection algorithms. Generally speaking, multi-pose face detection algorithms are mainly divided into the following two categories: based on traditional machine learning methods and based on depth learning method. [0004] ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem that face detection is easily affected by attit

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