Neural network-based face detection model training method, neural network-based face detection method and corresponding systems

Abstract

The present invention provides a neural network-based face detection model training method, a neural network-based face detection method, a neural network-based face detection model training system and a neural network-based face detection system. The training method includes the following steps that: the loss function of the bias network layer of a prediction face frame is calculated according to the bias information of the prediction face frame relative to a default face frame and the bias information of a real face frame relative to the default face frame; the loss function of the confidence network layer of the prediction face frame is calculated according to the confidence of the default face frame; the error of the two loss functions is calculated, and is fed back to a neural network, so that the weight of the neural network is adjusted; iterative training is repeated until convergence appears, so that a face detection model can be obtained, and therefore, the prediction face frame can contain a face more accurately. The detection method includes the following steps that: a face image to be detected is inputted to a trained face detection model, bias information and confidence are outputted; corresponding prediction face frames are calculated according to the bias information; and a prediction face frame corresponding to confidence greater than a preset confidence threshold or the highest confidence is selected as a 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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