Age recognition method and system based on face attributes

Abstract

The invention provides an age recognition method based on face attributes. The age recognition method is used for performing face recognition on a to-be-recognized image so as to judge a predicted agevalue of a person corresponding to the to-be-recognized image; the method is characterized by comprising the following steps: S1, constructing a deep neural network model based on classification regression, and training the deep neural network model by using a plurality of existing face images with corresponding actual age values as a training set, thereby obtaining a trained deep neural networkmodel as an age prediction model; S2, preprocessing the to-be-identified image to obtain a preprocessed identification image; and S3, inputting the preprocessed recognition image into an age prediction model to obtain an age expected value as a predicted age value.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to a face recognition method and system, in particular to a recognition method and system for age prediction of a face image. Background technique [0002] As an important biological characteristic of human beings, age information has many application requirements in the field of human-computer interaction, and has an important impact on the performance of face recognition systems. Age estimation based on face images refers to the application of computer technology to model the changes in face images with age, so that the machine can infer the approximate age or age range of a person based on the face image. If the age estimation problem based on face images is solved, then in daily life, various human-computer interaction systems based on age information will have great application demands in real life. For example, the age estimation system can be used to automatically determine the ...

AI Technical Summary

Problems solved by technology

[0003] In order to judge the age of the face image, some existing age recognition systems use the trained neural network to recognize the age attribute of the face. features, so the use of neural networks for age attributes will produce large errors

[0004] At the same time, the neural network needs a large number of data sets for training to reduce the error in the prediction process. However, in the existing face image data set, the face images with exact age values ​​are not enough to reduce the error of the neural network. This makes it impossible to accurately predict the age of the face

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