Human face recognition method and device based on residual-quantized convolutional neural network

Abstract

The invention provides a human face recognition method and device based on a residual-quantized convolutional neural network, in order to provide a human face recognition method and device capable ofperforming large-scale human face recognition and reducing the amount of calculation, thereby reducing hardware requirements and reducing training time. The human face recognition method comprises thefollowing steps: step S1, constructing a convolutional neural network model and performing training; step S2, preprocessing a target image and performing preprocessing on a to-be-determined image; step S3, sequentially inputting the preprocessed to-be-determined image and the preprocessed target image to a feature extraction model to obtain a to-be-determined feature vector and a target feature vector; step S4, determining the consistent human face image according to the target feature vector and the to-be-determined vector, wherein the step S1 comprises a step of setting a predetermined layer to be a quantized layer, performing integer bit quantization on a quantized layer parameter so as to approximate a parameter matrix of the quantized layer. The present invention also provides a human face recognition device based on the residual-quantized convolutional neural network.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to a face recognition method and device, in particular to a face recognition method and device based on a residual quantization convolutional neural network. Background technique [0002] Face recognition is to use the biometric information of the human face to identify the identity of the corresponding person through a certain technology. face image. Face recognition is an important research work in the field of computer vision and pattern recognition, and it also has rich practical application scenarios, such as community security, criminal pursuit, mobile payment and so on. [0003] Face recognition technology has been developed for decades, and many related machine learning algorithms have been proposed in the early years, including methods based on geometric features and methods based on statistics. However, affected by problems such as lighting brightness, posture, facial makeup...

AI Technical Summary

Problems solved by technology

When applied to face recognition, for the 3-channel input of the size of 224×224 corresponding to the usual face image size, the calculation process includes more than 4.2 billion floating-point multiplications, so the storage and calculation overhead is very huge. When the application In large-scale face recognition (for example, when the number of images to be determined is huge), it is more likely to have problems such as excessive parameters, large models, massive calculations, and low efficiency. This not only increases the hardware requirements for face recognition, It will also increase the time required for model training and even make the training process almost impossible to complete, making it difficult for the convolutional neural network based on the residual learning mechanism to be practically applied to face recognition tasks

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