Face recognition model training method and device, face recognition method and device, equipment and storage medium

Abstract

The invention relates to the field of biological recognition, trains a face recognition model based on deep learning, and particularly discloses a face recognition model training method and device, aface recognition method and device, equipment and a storage medium. The face recognition model training method comprises the steps: carrying out the training of a preset convolutional neural network,so as to construct a feature extraction network; establishing connection between the feature extraction network and a preset classification network to obtain a first convolutional neural network model; freezing a weight parameter of the feature extraction network of the first convolutional neural network model; performing iterative training on a classification network in the first convolutional neural network model to obtain a second convolutional neural network model; unfreezing the weight parameters of the feature extraction network of the second convolutional neural network model; and training the unfrozen second convolutional neural network model to obtain a face recognition model. The face recognition model training method can improve the face recognition speed and improve the stability of the model.

Description

technical field [0001] The present application relates to the field of biometrics, and in particular to a face recognition model training method, face recognition method, device, equipment and storage medium. Background technique [0002] In recent years, biometric detection and recognition represented by faces has been widely used in many fields such as identification and smart education. Face recognition technology refers to identifying the position of a face in a picture or a video through a face recognition model. Existing face recognition models mainly use transfer learning methods for training to speed up training. During the migration process, the classification layer is often added after the presentation layer of the network. Since the parameter distribution of the representation layer and the classification layer are inconsistent, there is a problem that the gradient explosion is easy to occur, resulting in poor stability of the model. Contents of the invention ...

AI Technical Summary

Problems solved by technology

Because the parameter distribution of the presentation layer and the classification layer are inconsistent, there is a problem that the gradient explosion is prone to occur, resulting in poor model stability.

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