Face age estimation method performing measurement learning based on convolutional neural network

Abstract

The invention discloses a face age estimation method performing measurement learning based on a convolutional neural network. The method comprises the overall steps that a dataset is constructed; thedataset is divided into a training set and a verification set; paired construction is performed on mini-batches on a network input layer, and then the mini-batches are sent into two twin networks fortraining; a VGG-16 network is constructed; network training is performed; softmax loss and revised contrastive loss are jointly used as supervisory signals to perform network adjustment; network evaluation is performed; and the finally estimated age is a maximum probability corresponding category obtained on a softmax layer. According to the method, deep learning and measurement learning are combined; by introducing measurement learning, the distinction degree of a feature space is higher, and therefore the robustness of an age estimation algorithm is higher; and deep learning is utilized to combine a feature extraction task and an objective function optimization task, end-to-end training is realized for the whole task, and good performance can be obtained when the method is applied to a public dataset.

Description

technical field [0001] The invention relates to an estimation method, in particular to a face age estimation method based on convolutional neural network for metric learning. Background technique [0002] As an important biological feature of human beings, age information has many application requirements in the field of human-computer interaction, and has an important impact on the performance of face recognition systems. Age estimation based on face image refers to the application of computer technology to model the law of face image change with age, so that the machine can infer the approximate age of a person or the age range to which they belong based on the face image. Using deep learning technology to achieve end-to-end feature extraction and objective function optimization is an important method for face age estimation. However, most of the current methods ignore the ordering of age and the correlation between ages, making it difficult for features to contain inform...

AI Technical Summary

Problems solved by technology

The problem with this technique is that the feature extraction and optimization functions are performed separately, so the extraction of features does not serve the optimization function better.

In addition, although the age distribution of this technology is not obtained by assuming variance, the age label of a single adjacent sample still has uncertainty, so its age distribution is still not ideal

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