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A face image age estimation method, device and terminal equipment thereof

A face image and image technology, applied in the field of convolutional neural network, can solve the problems of poor robustness, large number of databases, limited performance of face age estimation, etc., and achieve good robustness

Active Publication Date: 2021-01-01
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] However, the active appearance model trains the shape and texture features separately, losing the information of the common parts of the shape and texture features, and the active appearance model depends on each key point. Once the key points are not detected in place, the final performance will be affected.
The disadvantage of the feature subspace model is that when building the feature subspace model, multiple images of the same person with different ages are required to build the subspace, so the model will not be suitable for data with only a single age image
The disadvantage of the manifold model is that the manifold learning method of age characteristics requires a large number of databases, which is only suitable for large age databases, and the data distribution of each age group is required to be relatively uniform
The appearance model is the most used model. However, the features extracted by the appearance model are only hand-designed features, and the performance of face age estimation is very limited.
The existing face age estimation method based on the convolutional neural network model is poor in robustness, lacks direct modeling of face features and other features, and the learning ability of the network model is limited

Method used

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  • A face image age estimation method, device and terminal equipment thereof
  • A face image age estimation method, device and terminal equipment thereof
  • A face image age estimation method, device and terminal equipment thereof

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Experimental program
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Embodiment 1

[0033] see figure 1 , figure 1 It is a schematic flowchart of a method for estimating the age of a face image provided in Embodiment 1 of the present invention. As shown in the figure, the method may include the following steps:

[0034] Step S101, constructing a convolutional neural network model including a latent factorization layer.

[0035] In the embodiment of the present invention, the convolutional neural network model including the latent factorization layer refers to adding a latent factorization layer in the conventional convolutional neural network model, and the latent factorization layer is obtained by using the latent factor algorithm as Based on the basic construction, the features of the input image can be divided into two parts: age-related features and age-independent features (identity features), which can be expressed by the following formula:

[0036]

[0037] in, It is a common feature of the face extracted from the face image. Here, the feature e...

Embodiment 2

[0089] see Figure 5 , Figure 5 It is a schematic block diagram of a face image age estimation device provided by Embodiment 2 of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown.

[0090] The face image age estimation device can be a software unit, a hardware unit or a combination of software and hardware built in terminal equipment (such as mobile phones, tablet computers, notebooks, computers, etc.), or it can be integrated into the terminal as an independent pendant in the device.

[0091] Described a kind of human face image age estimating device comprises:

[0092] Model construction module 21, for constructing the convolutional neural network model comprising latent factorization layer;

[0093] Initialization module 22, is used for initializing described convolutional neural network model;

[0094] The training module 23 is used to input the preprocessed image into the initialize...

Embodiment 3

[0118] see Image 6 , Image 6 It is a schematic block diagram of a terminal device provided in Embodiment 3 of the present invention. The terminal device as shown in the figure may include: one or more processors 601 ( Image 6 only one is shown); one or more input devices 602 ( Image 6 Only one is shown in ), one or more output devices 603 ( Image 6 Only one shown in ) and memory 604. The aforementioned processor 601 , input device 602 , output device 603 and memory 604 are connected through a bus 605 . The memory 604 is used to store instructions, and the processor 601 is used to execute the instructions stored in the memory 604 . in:

[0119] The processor 601 is configured to construct a convolutional neural network model comprising a latent factorization layer; the processor 601 is configured to initialize the convolutional neural network model; the processor 601 is configured to input the preset The processed image is input to the initialized convolutional neur...

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Abstract

The invention belongs to the technical field of convolutional neural networks, and provides a face image age estimation method, device and terminal equipment. The method includes: constructing a convolutional neural network model including a latent factorization layer; initializing the convolutional neural network network model; input the preprocessed image into the initialized convolutional neural network model, and train the initialized convolutional neural network model by back-propagation method based on the preprocessed image based on the age loss function; The detected face image is input to the trained convolutional neural network model, and the age of the face in the to-be-detected face image is output. The latent factorization layer can decompose the features of the image into age-related components and age-independent components that need to be acquired, so it can perform training and detection based on age-related components, so that the convolutional neural network model has a relatively high performance. good robustness.

Description

technical field [0001] The invention belongs to the technical field of convolutional neural networks, and in particular relates to a face image age estimation method, device and terminal equipment thereof. Background technique [0002] Face age estimation has great application value in security monitoring, human-computer interaction, video retrieval and other fields. However, face age estimation is affected by many factors such as genes, living environment, and health status. Convolutional neural network has become a research hotspot in recent years due to its excellent performance in large-scale image processing. Among the existing face age estimation methods based on convolutional neural networks, there are mainly active appearance model (AAM), feature subspace model (AGES), appearance model (mainly BIF bionic features), and flow model. [0003] However, the active appearance model trains the shape and texture features separately, losing the information of the common par...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/084G06V40/16G06V40/178G06V40/172G06F18/24G06F18/214
Inventor 乔宇谭莲芝李志锋杜文斌
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI