Face age recognition method and device and storage medium

Abstract

The invention discloses a face age recognition method and device and a storage medium. The method comprises a face feature obtaining step: obtaining a face feature vector with a fixed number of channels based on a face age data set according to a backbone network of a face depth recognition model; an age model training step of training and generating age recognition models of a plurality of age classifications through a two-layer neural network according to the face feature vector; and an age recognition step: obtaining a to-be-recognized face picture, obtaining a to-be-recognized face featurevector according to the backbone network of the face depth recognition model, and obtaining a recognition age according to the age recognition model. According to the method, the existing face depthrecognition model is reused, the face feature vector processed in face recognition is obtained, and the shallow age model is trained and recognized, so that the recognition precision is improved, a depth age recognition learning model does not need to be repeatedly constructed, and manpower and material resources are saved.

Description

technical field [0001] The present invention relates to the technical field of face recognition, in particular to a face age recognition method, device and storage medium. Background technique [0002] Face recognition is a biometric technology for identification based on human facial feature information. With the maturity of its technology and the improvement of social recognition, face recognition is widely used in various fields. [0003] Usually, there are two types of methods for predicting age attributes based on faces. One is to extract the features of several key points through traditional image processing methods, and then use shallow methods to calculate and estimate the extracted features, so as to predict For specific age results, the advantage of this type of method is that it is faster, but the accuracy is very low; the other type is to use deep learning to learn and train a deep neural network model for age recognition. Although the accuracy is high, the calc...

AI Technical Summary

Problems solved by technology

[0003] Usually, there are two types of methods for predicting age attributes based on faces. One is to extract the features of several key points through traditional image processing methods, and then use shallow methods to calculate and estimate the extracted features, so as to predict For specific age results, the advantage of this type of method is that it is faster, but the accuracy is very low; the other type is to use deep learning to learn and train a deep neural network model for age recognition. Although the accuracy is high, the calculation speed is very low. slow

Images