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Neural-network-based large-scale unbalanced data face recognition method and system

A technology of unbalanced data and face recognition, applied in the field of face recognition, can solve the problems of long training process and low training efficiency, and achieve the effect of improving performance, efficient model training, and performance improvement

Active Publication Date: 2019-06-28
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

For a long time before, the method of metric learning has been a common method to deal with large-scale data. In terms of sampling, difficult sample mining is used to improve training efficiency. However, the training process is still very long and the training efficiency is low.

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Embodiment Construction

[0041] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than Full examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0042] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, rather than to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention...

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Abstract

The invention belongs to the field of face recognition, in particular to a neural-network-based large-scale unbalanced data face recognition method and system, and aims to solve the problems of large-scale data optimization and improvement of face recognition efficiency. According to the method, the performance of model face recognition is improved by improving the loss function and the sampling mode, the loss function of the self-adaptive boundary margin is provided in the aspect of the loss function to cope with unbalanced face data, and an improvement scheme is provided in the aspect of sampling for data sampling and classification template sampling. According to the method, model training can be efficiently carried out on large-scale unbalanced face data, and the performance is improved.

Description

technical field [0001] The invention belongs to the field of face recognition, and in particular relates to a face recognition method and system based on large-scale unbalanced data of a neural network. Background technique [0002] At present, most of the face recognition methods are based on the ideal balanced data with the same number of samples for each category. However, in actual situations, it is often necessary to face unbalanced data, that is, the distribution of samples in each category is extremely unbalanced. Some categories have a maximum of more than a thousand samples, while some categories have less than ten samples. At present, the most advanced methods (A-Softmax, AM-Softmax) are based on the research of balanced data. They set a fixed boundary margin (margin) for all categories, because only when each type of sample distribution is balanced , the scope of each category in the feature space is basically equal, so that the same boundary margin can be set fo...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
Inventor 雷震朱翔昱刘浩
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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