Convolutional neural network Training method and apparatus for human face identification and application

Abstract

The invention discloses a convolutional neural network training method and apparatus for human face identification and an application, and belongs to the field of human face identification. The method comprises: constructing a sample training library, wherein the sample training library comprises a plurality of sample classes, and the sample classes comprise human face image samples same in quantity; training a convolutional neural network by using the sample training library; extracting eigenvectors of all the human face image samples in the sample training library by using the trained convolutional neural network; classifying the eigenvectors by using a classifier; calculating classification accuracy of each sample class; and judging whether the convolutional neural network meets a set requirement or not, if yes, ending, otherwise, deleting a certain quantity of correctly classified human face image samples from the sample class with highest classification accuracy, adding the same quantity of human face image samples into the sample class with lowest classification accuracy, and re-training the convolutional neural network. The method avoids identification errors caused by influences of facial makeup and external environment, and avoids over-fitting.

Description

technical field [0001] The field of face recognition of the present invention particularly refers to a training method, device and application of a convolutional neural network for face recognition. Background technique [0002] With the rise of deep learning, especially the deepening of deep convolutional neural network research, a large number of network models based on convolutional neural network (CNN) have been applied to image processing and image recognition, especially in face recognition. Remarkable achievements have been made in the field. [0003] In the field of face recognition and authentication, there are usually such problems. For example, due to makeup and external environmental influences, two photos of different people may appear very similar, and two photos of the same person may be quite different. Such abnormal samples are an important reason for recognition errors. Contents of the invention [0004] The invention provides a training method, device ...

AI Technical Summary

Problems solved by technology

[0003] In the field of face recognition and authentication, there are usually such problems. For example, due to makeup and external environmental influences, two photos of different people may appear very similar, and two photos of the same person may be quite different.

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