A method and application of face recognition model based on ParaSoftMax loss function

Abstract

The invention discloses a method for constructing a face recognition model based on a ParaSoftMax loss function, which comprises the following steps: selecting a basic convolution neural network modelaccording to an application environment of a task; acquiring a face image marked with human face identity information in a specified number as a training data set; the decision edge parameters are obtained according to the difference of the class center angles between the difficult sample eigenvectors and the simple sample eigenvectors and the class center angles in the basic convolution neural network model. Obtaining a ParaSoftMax loss function according to the decision edge parameter; setting the loss function at the last layer of the basic convolution neural network model to form a face recognition model based on the loss function; input the training data set to the face recognition model, minimizing the loss function iterative training model parameters, and obtaining the optimal facerecognition model. Thus, the face recognition model of the present application can improve the accuracy of face recognition.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a face recognition training method based on a ParaSoftMax loss function. technical background [0002] Face recognition systems are widely used in face verification, access control, security monitoring, human-computer interaction and other fields. Face recognition tasks mainly include face verification and face identification. Currently, Convolutional Neural Networks (CNN) perform well in face recognition tasks, with an accuracy rate even surpassing that of the human eye. Therefore, the convolutional neural network is also the mainstream method to solve the problem of face recognition. [0003] The general process of the face recognition method using convolutional neural network technology is as follows: in the training phase, a large number of face training data of known categories are given, and the method of minimizing the loss function is used to iteratively solve ...

AI Technical Summary

Problems solved by technology

However, during classification training, the angle between difficult samples and class centers will be larger than the angle between similar simple samples and class centers, and even larger than the angle between itself and other class centers, resulting in misclassification into other classes.

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