Age estimation method and device, electronic equipment and computer readable storage medium

Abstract

The invention provides an age estimation method and device, electronic equipment and a computer readable storage medium to improve the accuracy of age estimation. The method comprises the steps that a target neural network receives a target image; the target neural network calculates a classification loss value according to the distance between the real age value and each age classification, the probability value corresponding to the real age and the probability value of each age classification in the training process, and performs parameter adjustment based on the classification loss value; wherein the target image is a mixed face image obtained by mixing different face images according to a preset proportion; the target neural network extracts a feature vector from the target image; the target neural network calculates a probability value corresponding to each age category based on the feature vector; the product of the probability value is added corresponding to each age category and the age deviation value to obtain an age prediction value; wherein the age deviation value corresponding to each age category is predicted in the training process, and the age deviation value belongs to a parameter.

Description

technical field [0001] The invention relates to the field of computers, in particular to an age estimation method and device, electronic equipment and a computer-readable storage medium. Background technique [0002] In some scenarios, it is necessary to use neural networks for age estimation based on face images. One way of estimation is to use age estimation as a classification task, that is, pre-design age categories (such as 0-100 years old, each integer age value is a category), and calculate the probability value of each age category by the neural network, and take the largest The age category corresponding to the probability value is used as the predicted age value. For example, assuming that the probability value of the age classification of 25 years old is 0.8, and its probability value is the largest among the 101 age classifications of 0-100, 25 years old will be selected as the predicted age value. [0003] During the training process, the classification loss f...

AI Technical Summary

Problems solved by technology

This makes traditional age estimation relatively inaccurate

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