Face age estimation method and system, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of mode recognition and digital image processing, and discloses a face age estimation method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a face age image set, and carrying out the preprocessing of the face age image set, and obtaining a preprocessed face age image set; constructing a face age estimation model; constructing a composite loss function containing error compression sorting loss and attribute guidance classification loss based on sorting labels; training a face age estimation model according to the face preprocessing image set to obtain a trained face age estimation model; and testing the trained face age estimation model according to a test data set to obtain a face age estimation result so as to realize face age estimation. According to the method, a high-performance multi-scale attention mechanism residual convolution unit, an attribute guidance module and a composite loss function containing error compression sorting loss are introduced, so that a face age estimation target which is robust, efficient and high in estimation performance index is achieved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and digital image processing, and in particular relates to a face age estimation method, system, electronic equipment and storage medium. Background technique [0002] At present, face age estimation is a kind of biometric identification technology based on human facial feature information to estimate human age information. With the continuous development of big data related technologies, face age estimation has been widely used in auxiliary identity authentication, human-computer interaction, demography and other fields. Among these tasks, age estimation based on face images has gradually become an important and challenging topic. [0003] Judging from the current research, according to the different ways of extracting features, face age estimation can be divided into two categories: methods based on traditional machine learning and methods based on deep learning. Since the manual f...

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Problems solved by technology

[0006] (1) Since the manual feature extraction method of traditional machine learning methods is limited by conditions such as a single facial pose and accurate positioning of facial feature points, its robustness is far less than that of deep learning methods supported by big data

[0007] (2) In the existing age estimation models, for the two characteristics of age growth: nonlinearity and continuity, classification and regression models can only tend to express one, and there is still room for development in the application of ranking models

[0008] (3) In the existing age estimation model, age information is highly correlated with label information such as gender and race, which has not been considered and studied in the field of age estimation

[0010] (1) There is a certain conflict in the expression of age characteristics (non-linearity and continuity), which often cannot be expressed well at the same time, and a loss function that satisfies both as much as possible is needed;

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