Multi-scale face age estimation method and system embedded with high-order information

Abstract

The invention relates to the field of face age estimation, in particular to a multi-scale face age estimation method and system embedded with high-order information, and the multi-scale face age estimation method comprises the steps: inputting a face image, and carrying out the preprocessing of the face image; inputting the face image into a residual network to perform global feature extraction soas to construct a global branch; inserting blocks for extracting high-order age information at different positions of the global branch; taking the output feature map of the first convolution layer of ResNets as the input of a long-term and short-term memory network, obtaining the position information of the age-sensitive region, and obtaining a local feature map through cutting to construct a local branch; minimizing a loss function through back propagation, performing joint optimization on the two branches, and performing iterative training on the neural network; and inputting the test setinto a trained neural network model, and calculating and outputting a final predicted age according to the age characteristics. The multi-scale face age estimation method and system have the advantages that the network model provided by the invention is relatively low in calculation cost and high in precision, and the applicability of related products is relatively high.

Description

technical field [0001] The invention belongs to the field of human face age estimation, in particular to a multi-scale human face age estimation method and system embedded with high-order information. Background technique [0002] The purpose of face age estimation is to automatically output biological age through face images, which is widely used in age-based face retrieval, accurate advertising, intelligent monitoring, human-computer interaction (HCI), Internet access control and other fields. An active research topic. Due to the combined effects of internal factors of facial aging (such as various genes) and complex changes in facial images (such as facial postures from different angles and camera vision), the facial aging process is uncontrollable and personalized, and accurate and reliable analysis of facial images Automatically estimating age is extremely challenging. [0003] The classical age estimation algorithm consists of two consecutive but relatively independe...

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, the design of deep convolutional neural network mainly focuses on deeper or wider network to enhance the nonlinear modeling ability of the model, but the face age estimation method based on deep learning cannot take into account the overall situation well. - The problem of facial age feature expression in local details limits the feature expression ability of CNN to a certain extent

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