Target recognition network training method and device, computer equipment and storage medium

Abstract

The invention relates to a target recognition network training method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a weight corresponding toa feature value of each sample image in a sample set, and determining a classification allowance parameter corresponding to the feature value of each sample image according to a distance difference value between an unknown weight corresponding to the feature value of each sample image and a known weight corresponding to the feature value of each sample image and a preset boundary angle; and thenfurther constructing a loss function according to the weight and the classification margin parameter, and finally training the target recognition network by using the constructed loss function. According to the loss function constructed by the method, the network quality of the target recognition network trained by using the loss function can be improved, so that the face recognition precision when the trained target recognition network is used for face recognition is improved.

Description

technical field [0001] The present application relates to the technical field of face recognition, and in particular to a training method, device, computer equipment and storage medium for a target recognition network. Background technique [0002] With the development of face recognition technology, the application of deep neural network models to various types of face recognition fields has received extensive attention, for example, face verification, face retrieval, face clustering and other fields. The quality of the above-mentioned deep neural network model directly affects the results of face recognition tasks. Therefore, how to improve the quality of the deep neural network model through training has become a more concerned issue now. [0003] At present, for the training process of deep neural network models, the commonly used classification learning scheme is to use the loss function of Margin Softmax loss to optimize the classification model. Among them, Softmax lo...

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Problems solved by technology

[0004] However, the above-mentioned classification learning method has the problem that the quality of the deep neural network model obtained by training is low, and then the recognition accuracy is low when using the deep neural network model for face recognition.

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