A multi-label age estimation method based on convolution neural network

A convolutional neural network and multi-label technology, applied in the field of multi-label age estimation based on convolutional neural network, can solve problems such as poor robustness and inaccurate age estimation, achieve short running time, alleviate uneven age distribution, very robust effect
CN109101938AActive Publication Date: 2018-12-28武汉嫦娥医学抗衰机器人股份有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
武汉嫦娥医学抗衰机器人股份有限公司
Publication Date
2018-12-28

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Abstract

The invention discloses a multi-label age estimation method based on a convolution neural network, which comprises the following steps: an input sample data set is obtained; face detection is carriedout on each input sample, alignment is carried out, and normalization is carried out according to the face position; the age tags of the input samples are subjected to multi-tag processing so that each sample is mapped to the same number of tags. all the normalized images are used as the input of the convolution neural network, and the multi-tag set is used as the output to train the convolution neural network, and the age estimation model is obtained; according to the principle of binary classification output and multi-label processing, combined with the ordered characteristics of age, the age of human face estimation is calculated. The invention utilizes the micro-variability and the orderliness between age features, constructs a convolution neural network model by using the idea of multi-label learning, and solves the problems of low age estimation accuracy and poor robustness existing in the existing age estimation method.
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Description

technical field

[0001] The invention belongs to the technical field of image processing and deep learning, and more specifically relates to a multi-label age estimation method based on a convolutional neural network. Background technique

[0002] At present, face age estimation has been widely used in the fields of investigation and monitoring, information management, intelligent human-computer interaction, and social entertainment. However, face age estimation technology is not accurate enough in real application scenarios and is easily affected by expressions, poses, and lighting conditions.

[0003] In face age estimation methods, most of them use traditional age estimation algorithms. Traditional age estimation methods are mainly divided into two stages: feature extraction and age estimation. In the feature extraction stage, most of them are explicit feature extraction, and the age features based on manual design are obtained. However, due to the limitations of manual ...

Claims

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