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Face detection method and device

A technology of face detection and detection frame, which is applied in the direction of instruments, character and pattern recognition, computer components, etc. Performance improvement, accurate classification and prediction

Inactive Publication Date: 2018-04-03
BEIJING EYECOOL TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a face detection method to solve the problem that the detection performance and calculation speed of face detection in the prior art cannot meet the needs of users at the same time

Method used

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  • Face detection method and device

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Experimental program
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Embodiment 1

[0043] refer to figure 1 , a flow chart of a face detection method provided by an embodiment of the present invention is given.

[0044] Step 101, using the pre-trained first convolutional neural network model to classify the image to be tested, determine the first face confidence of each input area in the image to be tested, and obtain the Filter out at least one candidate region from the input region.

[0045] Wherein, the first convolutional neural network includes m convolutional layers.

[0046] Specifically, the first convolutional neural network is a deep convolutional neural network with deep learning capabilities, including one or more convolutional layers and pooling layers, capable of deep learning. Compared with other deep learning structures, deep convolutional Neural networks show even more outstanding performance in image recognition.

[0047] Before detecting the face, the image classification task of the first convolutional neural network can be trained by ...

Embodiment 2

[0058] refer to figure 2 , on the basis of the above embodiments, this embodiment further discusses the face detection method.

[0059] In an optional embodiment, before performing face detection on the image, training the first convolutional neural network model and the second convolutional neural network model is also included.

[0060] The following are respectively Figure 2 to Figure 4 The embodiment discusses the process of training the first convolutional neural network model and the second convolutional neural network model.

[0061] refer to figure 2 , a flow chart of training the first convolutional neural network model in a face detection method provided by an embodiment of the present invention is given:

[0062] Step 201, selecting a face data set including face annotations as a training sample, and clipping a training image in the training sample.

[0063] Optionally, use the WIDER FACE dataset as a training sample, where the WIDER FACE dataset contains ric...

Embodiment 3

[0126] On the basis of the above embodiments, this embodiment also provides a face detection device, which is applied to an artificial intelligence terminal.

[0127] refer to Figure 13 A structural block diagram of a face detection device provided by an embodiment of the present invention is given, which may specifically include the following modules:

[0128] The pre-classification module 1301 is configured to use the pre-trained first convolutional neural network model to classify the image to be tested, determine the first face confidence of each input area in the image to be tested, and according to the first face The confidence level screens out at least one candidate region from the input region, and the first convolutional neural network includes m convolutional layers.

[0129] The secondary classification module 1302 is configured to use the pre-trained second convolutional neural network model to classify the candidate areas respectively, determine the second face...

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Abstract

The embodiment of the invention provides a face detection method and device, and the method comprises the steps: employing a pre-trained first convolution neural network model for the classification of a to-be-detected image, screening out at least one candidate region from a to-be-detected image input region, employing a pre-trained second convolution neural network model for the classification of the candidate regions, screening out at least one selected region from the candidate regions, carrying out the removing and clustering of detection frames according to at least one selected region,so as to obtain a human face detection region. Because the input region of the first convolution neural network is very small, the calculation speed of human face detection is improved. In addition, because the two convolution neural network models at different depths are employed, the secondary classification of the obtained candidate regions is carried out, and the classification prediction is enabled to be more accurate. Meanwhile, a large number of false detection samples are filtered out, and the detection performances are improved.

Description

technical field [0001] Embodiments of the present invention relate to the field of artificial intelligence, and in particular to a face detection method and device. Background technique [0002] Face detection refers to the process of determining the location and size of all faces from an input area. As a key technology in face information processing, face detection is the premise and foundation of many automatic face image analysis applications, such as face recognition, face registration, face tracking, face attribute recognition, etc. The first step in the human-computer interaction system. Not only that, most of the current digital cameras are embedded with face detection technology to automatically focus, and many social networks such as FaceBook use face detection technology to achieve image annotation. [0003] With the development of artificial intelligence, face detection methods have also been developed to a certain extent, but there are still some deficiencies. ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/00
CPCG06V40/161G06V40/172G06F18/214G06F18/24
Inventor 段旭宋丽张祥德
Owner BEIJING EYECOOL TECH CO LTD
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