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...

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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 spe

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