Human face detection method and detection device based on multi-task cascade-connection convolution neural network

Abstract

The invention discloses a human face detection method and a detection device based on multi-task cascade-connection convolution neural network, wherein the method comprises: establishing a cascade-connection multi-level convolution neural network; using the human face front samples, the human face back samples, some parts of the human face and the human face's key point samples as the training samples to train the multi-level convolution neural network to learn the tasks of human face categorizing, human face area position regression and human face's key point positioning; and utilizing the well trained multi-level convolution neural network to make human face detection from the to-be-detected image wherein in the training stage, both the online manner and the offline manner are combined to extract the human face back samples as the training samples. According to the invention, based on the cascade-connection multi-level convolution neural network, it is possible to learn the characteristics with stronger robustness, and at the same time, through the combination of the online manner and the offline manner to extract the back samples, the categorizing capability of the network is enhanced, so that the detection capability and the accuracy of the network are increased, and that the running speed of the method in an actual product is ensured.

Description

technical field [0001] The present invention relates to the field of face detection, in particular to a face detection method and detection device based on a multi-task cascaded convolutional neural network. Background technique [0002] Face detection technology is the basis of all face-related technologies (face recognition, face alignment, face expression recognition, face key point prediction, etc.). As the face detection technology is applied to more and more scenes, especially in the monitoring environment where the user does not cooperate, the scale, angle and illumination of the face change greatly, which requires more and more accuracy and speed of face detection. higher. [0003] Deep learning (deep learning) technology is not essentially different from the artificial neural network in the early years. Since 2012, benefiting from the increase of data and the enhancement of computing power, deep learning technology has developed rapidly. Convolutional neural netwo...

AI Technical Summary

Problems solved by technology

[0007] The invention provides a face detection method and detection device based on a multi-task cascaded convolutional neural network to solve the technical problem in the prior art that it is difficult to take into account both computational complexity and detection accuracy

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