A real-time robust face detection method

Abstract

The invention relates to computer vision recognition technology, specifically a real-time robust face detection method, comprising the steps of: acquiring a target image for face detection and performing preprocessing; establishing and training a detection model, the detection model including a plurality of convolution modules , multiple Inception modules, multiple Inception modules with residuals, and multiple detection modules. The Inception module is a channel separation convolution module with two branches, and the Inception module with residuals is a multi-branch with residual connections. The channel separation convolution module, the detection module uses the convolution operation to finally output the position information and classification information; input the target image into the trained detection model, and obtain the convolution results at the specified level respectively; classify and classify the obtained convolution results Regression; Calculate the position of the face based on the regression and classification results. This method constructs a simple and efficient convolutional neural network, reduces redundant operations in the detection process, and can achieve real-time effects on the CPU.

Description

technical field [0001] The invention relates to computer vision recognition technology, in particular to a real-time robust face detection method. Background technique [0002] Face detection is a sub-topic of target detection in computer vision. It belongs to a specific category of target detection. It is a classic problem that has been deeply studied in machine vision. It has important application value. The goal of face detection is to find the location of a face in an image. Since Viola and Jones proposed the use of Haar-like features and cascaded AdaBoost classifiers to detect faces in 2001, new features (such as LBP, Bow, HOG, etc.) and new detectors (such as SVM, LatentSVM, DPM )Been proposed. Among them, the DPM (Deformable Part Model) algorithm is a component-based detection algorithm, which has a good detection effect on distorted, multi-pose, and multi-angle faces. [0003] At present, detection algorithms based on artificially designed features are easily aff...

AI Technical Summary

Problems solved by technology

[0007] The detection speed of the Cascade CNN method is greatly affected by the number of targets to be detected. The larger the number of targets, the more regions need to be screened, and the slower the detection speed; the two-stage method has more suggested regions to extract. The detection speed is generally slow; hyperparameters such as the number, size, and aspect ratio of the prior box in the one-stage method affect the training and detection speed of the model

[0008] In addition, the backbone network in the existing face detection model is migrated from the general target detection backbone network. The complexity of the model itself is high and the appearance modeling of the face generally contains redundant information. The detection process requires computing power. Supported by powerful GPU, etc., it is still difficult to achieve real-time on general mobile terminals and CPUs

[0009] The slow detection speed of the existing face detection method based on deep neural network on the CPU is mainly caused by the high complexity of the model itself; in the one-stage detection method, the unreasonable setting of the prior frame will also affect the detection accuracy and speed

Images