Face age bracket recognition method and device, computer device, and readable storage medium

A recognition method and age group technology, applied in the field of computer vision, can solve the problems of dimensionality disaster, long time to extract age group features, low time efficiency, etc., achieve time efficiency improvement, improve feature extraction problems, and reduce computational complexity Effect

Active Publication Date: 2018-05-11
SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the convolutional neural network needs to continuously perform convolution calculations on the image, and it takes a long time to extract the characteristics of the age group, and the time efficiency is low.
The age growth pattern subspace needs to splice the features of all age groups into a large vector, which can easily lead to the disaster of dimensionality

Method used

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  • Face age bracket recognition method and device, computer device, and readable storage medium
  • Face age bracket recognition method and device, computer device, and readable storage medium
  • Face age bracket recognition method and device, computer device, and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] figure 1 It is a flow chart of the multi-layer stacked autoencoder model training method provided by Embodiment 1 of the present invention. The multi-layer stack autoencoder model training method is applied to a computer device. The multi-layer stacked self-encoding model training method trains a multi-layer stacked self-encoding model suitable for face age group recognition (that is, age group recognition based on face images), so as to be applied to security control, video surveillance, electronic customer relationship management, etc.

[0050] Such as figure 1 As shown, the multi-layer stacked self-encoder model training method specifically includes the following steps:

[0051] 101: Obtain face features of each face image in the training sample set.

[0052] The training sample set of the multi-layer stacked autoencoder model includes multiple age-labeled face images, and each face image is a training sample. For example, the training sample set includes 4,000 ...

Embodiment 2

[0114] figure 2 It is a flow chart of the face age group recognition method provided by Embodiment 2 of the present invention. The face age group recognition method is applied to a computer device. The face age group recognition method can be applied to occasions such as security control, video surveillance, and electronic customer relationship management. The method trains a multi-layer stacked self-encoding model, and uses the trained multi-layer stacked self-encoding model to identify the face age group of the face image to be processed.

[0115] Such as figure 2 As shown, the face age group recognition method specifically includes the following steps:

[0116] 201: Obtain a training sample set of a multi-layer stacked autoencoder model, and acquire face features of each face image in the training sample set.

[0117] Step 201 in this embodiment is consistent with step 101 in Embodiment 1. For details, please refer to the relevant description of Step 101 in Embodiment...

Embodiment 3

[0137] image 3 A structural diagram of a multi-layer stacked self-encoder model training device provided in Embodiment 3 of the present invention. The multi-layer stacked self-encoder model training device 10 is applied to a computer device. The multi-layer stacked self-encoding model training device 10 trains a multi-layer stacked self-encoding model suitable for face age group recognition (that is, age group recognition based on face images), so as to be applied to security control, video surveillance, electronic customer relationship management, etc.

[0138] Such as image 3 As shown, the multi-layer stacked autoencoder model training device 10 may include: an acquisition unit 301 , a pre-training unit 302 , an adjustment unit 303 , and a judgment unit 304 .

[0139] The obtaining unit 301 is configured to obtain the face features of each face image in the training sample set.

[0140] The training sample set of the multi-layer stacked autoencoder model includes multi...

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Abstract

The invention discloses a face age bracket recognition method, and the method comprises the steps: (a), obtaining the face features of all face images in a training sample set; (b), carrying out the pre-training of a multilayer stack-type self-coding model; (c), carrying out the coding of the face features of all face images, and obtaining the age bracket features of all face images; (d), carryingout the clustering of the age bracket features of all face images, and obtaining a preset number of clustering centers; (e), calculating the membership degree of the age bracket features of all faceimages to all clustering degrees, and adjusting the network parameters of the multilayer stack-type self-coding model, so as to optimize the membership degree; (f), repeatedly carrying out the steps (c)-(e) till a preset condition is met; (g), carrying out the coding of a to-be-processed face image, and obtaining the age bracket features of the to-be-processed face image; (h), carrying out the agebracket recognition of the to-be-processed face image, and obtaining the age bracket type of the to-be-processed face image. The invention also provides a face age bracket recognition device, a computer device, and a readable storage medium. The method can achieve the quick and efficient recognition of a face age bracket.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a method and device for recognizing age groups of faces, a computer device and a computer-readable storage medium. Background technique [0002] Age group identification is a new research direction in the field of biometric identification. Accurate identification of age groups has great application prospects, such as in security control, video surveillance, and electronic customer relationship management. [0003] The human face contains a large amount of age-related information, and age group recognition (ie, face age group recognition) can be performed according to the face image. Existing face age recognition technologies include convolutional neural network, age growth pattern subspace, etc. However, the convolutional neural network needs to continuously perform convolution calculations on the image, and the time for extracting age group features is relativel...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/178G06V40/168G06V40/172G06F18/214
Inventor 杨龙游德创
Owner SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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