Age prediction method based on hybrid double models

Abstract

The invention discloses an age prediction method based on hybrid double models. The age prediction method comprises the following steps: constructing a first deep neural network; initializing the first deep neural network; obtaining a soft label of a face age picture; constructing a first target function, performing loss calculation on the output of the first deep neural network and the soft labelby adopting the first target function, and updating the first deep neural network; constructing a second deep neural network; initializing the constructed second deep neural network; obtaining a label distribution label of the face age picture; constructing a second target function, performing loss calculation on the output of the second deep neural network and the label distribution label by adopting the second target function, and updating the second deep neural network; and using the trained first deep neural network and second deep neural network to perform result extraction on a to-be-tested photo, and predicting the age. According to the method, two networks adopting the new inventive method are integrated, so that the accuracy of age prediction can be effectively improved, and errors can be effectively reduced.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to an age prediction method based on a mixed dual model. Background technique [0002] With the development of artificial intelligence, deep learning is being applied in all aspects of daily life, and age recognition can also be solved through this technology. Age prediction can be used in human-computer interaction. For example, in vending machines with cameras, age prediction algorithms can be used to determine whether consumers have reached the legal age to buy cigarettes and alcoholic beverages. Age prediction can also be used for demographic searches to narrow down search targets by setting an age range. There are many other applications of age prediction for mining. However, the current age prediction effect does not meet the requirements of accurate prediction very well. This is because age is a slow process, so accurate age prediction is often not easy to obtain; ...

AI Technical Summary

Problems solved by technology

However, the current age prediction effect does not meet the requirements of accurate prediction.

This is because age is a slow process, so accurate age prediction is often not easy to obtain; and the aging process of different people is affected by their genes and living environment

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