Training of face detection model based on neural network, face detection method and system

Abstract

The present invention provides a neural network-based face detection model training, face detection method and system, the training method: according to the bias information of the predicted face frame relative to the default face frame, and the real face frame relative to the default Based on the bias information of the face frame, calculate the loss function of the predicted face frame bias network layer; calculate the loss function of the predicted face frame confidence network layer according to the confidence of the default face frame; calculate the error of the two loss functions And the error is fed back to the neural network to adjust the weights in the neural network; repeated iterative training until the face detection model is converged, so that the predicted face frame contains the face more accurately. Detection method: Input the face image to be tested into the trained face detection model to output bias information and confidence; calculate the corresponding predicted face frame according to the bias information; select a confidence greater than the preset confidence threshold Or the predicted face frame corresponding to the highest confidence level is used as the face detection result.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a neural network-based face detection model training, face detection method and system. Background technique [0002] The main task of face detection is to judge whether there is a face on a given face image, and if there is a face, give the position and size of the face. The commonly used process of face detection mainly includes the following three steps: (1) select a rectangular area from the image as an observation window; (2) extract features from the observation window to describe the content it contains; (3) carry out Classification discrimination, to determine whether the window contains a face. By continuously repeating the above three steps, until all observation windows on the face image are traversed. If any observation window is judged to contain a human face, the position and size of the window can be the position and size of the detected human face; conv...

AI Technical Summary

Problems solved by technology

[0006] (1) At present, most face detectors use a sliding window method to select the observation window, so after traversing a face image, it is necessary to calculate and distinguish a large number of observation windows, so the calculation amount is relatively large; and for face images For faces of different sizes, it is necessary to build an image pyramid, or use observation windows of different scales, and the face detection speed is slow

[0007] (2) Most face detection algorithms have many steps, and each step is relatively independent. Any problem in any step will affect the final face detection result

[0008] (3) The face detection method based on deep learning, although the effect is good, needs to scale the input face image to a fixed size, causing the face in the image to be stretched, distorted, deformed, etc., which affects the final face detection result

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