A Method for Age Estimation of Face Image Based on Three-level Residual Network

Abstract

A method for estimating the age of face images based on a three-level residual network belongs to the field of data processing technology, and the purpose is to improve the age estimation level of face images under unrestricted conditions. Based on the difference network framework, a three-level residual network is established; then the three-level residual network is used to pre-train the ImageNet dataset to obtain the ImageNet residual network model; The obtained ImageNet residual network model is fine-tuned for training; finally, the fine-tuned trained three-level residual network is used to estimate the age of face images. The present invention uses a three-level residual network to realize face image age estimation, which not only greatly improves the learning ability of the DCNN network model, but also well solves the problems of over-fitting and gradient disappearance in the training process, thereby improving the non-responsive Accuracy of face image age estimation under limited conditions.

Description

technical field [0001] The invention relates to a method for accurately estimating age according to face images under unrestricted conditions, and belongs to the technical field of data processing. Background technique [0002] Face is an extremely rich source of information, and people can obtain a lot of useful information from face images, such as identity, gender, age, and expression. As one of the key information of human face, age plays a fundamental role in people's social interaction, so relying on facial images to realize automatic age estimation is one of the important tasks in the field of artificial intelligence. At present, face age estimation has good application prospects in many intelligent fields such as age-based human-computer interaction, access control, visual surveillance, marketing, and law enforcement. [0003] The main idea of ​​face image age estimation is to extract the main features from the face image, and then use classification or regression m...

AI Technical Summary

Problems solved by technology

[0008] 3. A deeper DCNN network will lead to the problem of gradient disappearance, which seriously hinders the learning process of the classification model, limits the learning ability of the DCNN network, and affects the accuracy of age estimation of face images

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