A three-category face detection method using contextual information

Abstract

The invention discloses a three-category human face detection method utilizing context information, comprising the following steps: Step 1, data preparation: a, manually labeling each human face in an image collection, and obtaining a human face frame; b, Classify the marked face frames according to the size, and enlarge the small face area, and mark the rest as normal faces; c. Divide the set of marked face images obtained in step b into a training set and a verification set ;Step 2, model design: design an end-to-end neural network model, including basic convolutional network, region proposal network and fine-tuning network; step 3, model training: input the neural network model designed in step 2 through the training set, and use batch The stochastic gradient descent method is used for model training; the verification set is used to verify the model training effect; finally, the face detection model is obtained. The invention greatly improves the accuracy of face detection and the recall rate of faces.

Description

technical field [0001] The invention relates to a human face detection method, in particular to a three-category human face detection method using context information. Background technique [0002] Face detection means that for any given image, a certain strategy is used to search it to determine whether it contains a human face (existence), and then return the position, size and posture of the human face. Face processing and analysis include face recognition, face tracking, pose estimation and expression recognition, among which face detection is the key first step in all face information processing. Most of the current face detection methods are based on the deep neural network framework. The main methods are: [0003] 1) Face detection based on cascaded convolutional neural network. Related patent: CN107688786A. Main technical means: Firstly, image preprocessing is carried out, and the test image is scaled and input to the first-level network. Secondly, in the subseq...

AI Technical Summary

Problems solved by technology

Problems and disadvantages: This type of method needs to set the minimum face parameters, and has a strong impact on the detection results; secondly, the network depth in the first stage is average, resulting in low face recall and a decrease in accuracy

Problems and disadvantages: The accuracy of extracting the foreground area from the picture to be checked by using the skin color segmentation method is not good, and the robustness to people with different skin colors is weak, which directly leads to inaccurate selection of samples based on the candidate area used for training; secondly, for the scale in the picture Smaller faces, poor detection ability

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