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A gender recognition method, device and computing device based on a multi-output convolutional neural network

A convolutional neural network and gender recognition technology, applied in the field of gender recognition methods, devices and computing equipment based on multi-output convolutional neural networks, can solve problems such as single data and unsatisfactory results

Active Publication Date: 2019-06-04
XIAMEN MEITUZHIJIA TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, for a class with a small number of samples, because the data will be too single, satisfactory results cannot be obtained when the situation is complex

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  • A gender recognition method, device and computing device based on a multi-output convolutional neural network
  • A gender recognition method, device and computing device based on a multi-output convolutional neural network
  • A gender recognition method, device and computing device based on a multi-output convolutional neural network

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Embodiment Construction

[0027]Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0028] figure 1 is a block diagram of an example computing device 100 . In a basic configuration 102 , computing device 100 typically includes system memory 106 and one or more processors 104 . A memory bus 108 may be used for communication between the processor 104 and the system memory 106 .

[0029] Depending on the desired configuration, processor 104 may be any type of processing including, but not limited to, a microprocesso...

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Abstract

The invention discloses a gender recognition method, device and computing device based on a multi-output convolutional neural network. The method includes: obtaining face image data from an image database, which includes face images and face gender; Image data, the first convolutional neural network is trained, and the first neural network includes the first convolutional layer, the first downsampling layer, the second convolutional layer, the second downsampling layer, and the first fully connected layer connected in sequence and the second fully connected layer; add the third fully connected layer and the fourth fully connected layer in the trained first convolutional neural network to generate the second convolutional neural network; according to the face image data, the second convolution The neural network is trained; the face image to be recognized is input into the trained second convolutional neural network, and the first gender output of the second fully connected layer output and the second gender output of the fourth connected layer output are obtained; according to the first The first gender output and the second gender output determine the gender of the face image to be recognized.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a gender recognition method, device and computing device based on a multi-output convolutional neural network. Background technique [0002] As one of the important biological characteristics, the face image contains a lot of information, such as gender, age, race, etc. With the further development of face image research in image processing technology, especially in face gender recognition, in addition to traditional manual feature extraction methods such as PCA and LBP, convolutional neural networks (CNN: Convolutional NeuralNetwork) Based on face gender recognition methods have gradually developed. [0003] However, in the existing methods of facial gender recognition using convolutional neural networks, when training convolutional neural networks, due to the uneven distribution of data, the prediction results will be closer to the class with a larger number of sample...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06V40/168G06V40/172
Inventor 曾志勇许清泉张伟洪炜冬
Owner XIAMEN MEITUZHIJIA TECH