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

Abstract

The invention discloses a face detection method based on a multi-scale cascaded densely connected neural network, belongs to the field of image processing and computer vision, and is suitable for intelligent systems such as face recognition, face expression recognition, and driver fatigue detection. The present invention includes a method for constructing a region nomination network and a method for constructing a multi-level densely connected convolutional network model, specifically including: collecting face pictures marked with bounding box information of faces, forming a network that meets the input conditions of each sub-network The training data set; construct a cascaded densely connected neural network with strong generalization ability; use the training data set to train each sub-network separately, and obtain the overall network model; finally use the overall network model to detect multi-pose people in the picture Face. The present invention introduces a dense connection mode into the network, so that the network can fully extract face feature information, thereby improving the accuracy of face detection under multiple poses.

Description

technical field [0001] The invention belongs to the fields of image processing and computer vision, in particular to a face detection method based on a multi-scale cascade densely connected neural network. Background technique [0002] Face images contain rich 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: traditional machine learning methods and depth-based methods. learning method. [0004] Traditiona...

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 attitude changes, and to provide a face detection method based on a multi-scale cascaded densely connected neural network

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