Multi-scale fast human face detection method for convolutional neural network feature fusion

A convolutional neural network and feature fusion technology, applied in the field of computer digital image processing and pattern recognition, can solve the problems of poor detection frame accuracy, high missed detection rate and false detection rate

Active Publication Date: 2018-09-11
台州智必安科技有限责任公司
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Problems solved by technology

[0005] The purpose of the present invention is to provide a multi-scale fast face detection method with deep feature fusion, which can locate the positions of all faces in the ...

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  • Multi-scale fast human face detection method for convolutional neural network feature fusion
  • Multi-scale fast human face detection method for convolutional neural network feature fusion
  • Multi-scale fast human face detection method for convolutional neural network feature fusion

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[0067] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0068] Such as figure 1 Shown, be the multi-scale fast face detection method of a kind of convolutional neural network feature fusion of the present invention, comprise the steps:

[0069] Step 1, using the SSD (Single Shot Multibox Detector) target detection algorithm as the basic framework, see the literature for details: W.Liu, D.Anguelov, D.Erhan, C.Szegedy, S.Reed, C.-Y.Fu, and A.C.Berg.SSD:Single shot multibox detector.ECCV,pages 21–37,2016. Improve the feature extraction method in SSD, adding feature fusion method:

[0070] Such as figure 2As shown, SSD is a multi-scale target detection method. The detector performs classification and detection frame regression ...

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Abstract

The invention discloses a multi-scale fast human face detection method for convolutional neural network feature fusion. The method comprises the following steps: step 1, based on a model structure ofan SSD fast target detection method, a feature extraction method in the SSD is transformed, a feature fusion method is added, and a modified detection model is obtained; step 2, human face detection training is carried out on the detection model transformed in the step 1, and a trained deep neural network model is obtained; step 3, the to-be-detected picture is calculated by using a deep neural network model trained in the step 2, to obtain a model output result. According to the invention, the human face in the image can be quickly recognized and the human face can be accurately positioned, so that the human face is separated from the complex background, and a foundation is provided for identity verification and tracking of people in an image.

Description

technical field [0001] The invention belongs to the technical field of computer digital image processing and pattern recognition, and in particular relates to a face detection method. Background technique [0002] With the increase in the number of surveillance cameras in our country, massive amounts of surveillance video data are generated every day. In this context, computer-aided surveillance video content analysis technology becomes necessary and urgent. In surveillance video, the objects we monitor are mainly people, and facial features are the most important information for us to use image information for identification and verification. Face detection can locate the position of all faces in the picture and separate the face from the background, which is the premise of subsequent face representation and face recognition. Therefore, face detection is the first step in surveillance video content analysis. [0003] The current methods of target detection mainly include...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/161G06V40/168G06V40/172G06N3/045G06F18/253
Inventor 钱学明韩振张宇奇邹屹洋侯兴松
Owner 台州智必安科技有限责任公司
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