Face detection method based on convolutional neural network

A convolutional neural network and face detection technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of insufficient detection accuracy and detection speed, and achieve reduced test time, reduced complexity, and high speed Effect

Inactive Publication Date: 2015-04-08
NANJING AIKELESI NETWORK TECH CO LTD
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AI Technical Summary

Problems solved by technology

The current face detection algorithm is not able to deal with changing conditions such as arbitrary posture, illumination and occlusion, and there are deficiencies in detection accuracy and detection speed.

Method used

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  • Face detection method based on convolutional neural network
  • Face detection method based on convolutional neural network
  • Face detection method based on convolutional neural network

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Embodiment Construction

[0029] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0030] Such as figure 1 The flow chart of the present invention is shown, the face detection method based on convolutional neural network, including a training phase and a testing phase.

[0031] In the training phase, it is first necessary to collect training samples. In this embodiment, a total of 200,000 training samples are collected. Among these 200,000 samples, 100,000 are non-human face pictures, 10,000 are human face pictures, and the remaining 9 The 10,000 samples are the face pictures obtained from the aforementioned 10,000 face pictures by adding Gaussian white noise, picture rotation, color conversion and other image processing methods. Then these samples are input into the convolutional neural network for training, and the connection weights and bias values ​​of the convolutional neural network are obtained.

[0032] In this ...

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Abstract

The invention discloses a face detection method based on a convolutional neural network. The face detection method comprises a training stage and a testing stage, wherein the training stage comprises the following steps: inputting a training sample into the convolutional neural network, and obtaining a connection weight and an offset value of the convolutional neural network. The testing stage comprises the following steps: reading a video image; when a moving object is detected, extracting an interested area, moving for one pixel each time by utilizing a block with a n*n pixel size, and carrying out partitioning processing to the interested area to obtain a plurality of pictures; zooming the partitioned pictures to a size which is the same with the size of the training sample; and inputting the pictures into the convolutional neural network which finishes training for classification, wherein n is greater than or equal to 50 and less than or equal to 70. The convolutional neural network is used as a classifier, so that detection precision and speed can be improved.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a face detection method based on a convolutional neural network. Background technique [0002] Face detection means that for any given image, a certain strategy is used to search it to determine whether it contains a human face, and if so, return the position, size and posture of the human face. Face detection is a key link in automatic face recognition system. Early face recognition research mainly focused on face images with strong constraints (such as images without background), often assuming that the face position is certain or easy to obtain, so the face detection problem has not been taken seriously. With the development of e-commerce and other applications, face recognition has become the most potential biometric authentication method. This application background requires automatic face recognition systems to have certain recognition capabilities for...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06N3/084G06V40/173
Inventor 韩晓东罗坤吴长春戴铁军何亮
Owner NANJING AIKELESI NETWORK TECH CO LTD
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