Face recognition method based deep learning and face recognition device thereof and electronic equipment

Abstract

The invention discloses a face recognition method based deep learning and a face recognition device thereof and electronic equipment. The method comprises the steps that a convolutional neural network model is constructed, and the convolutional neural network model comprises a first convolution unit, a first pooling layer, multiple convolution combinations, a second pooling layer and full connection layers which are connected in turn, wherein the first convolution unit comprises a first convolution layer, a batch normalization layer and an excitation function layer, the excitation function layer simultaneously uses a ReLU function and a NReLU function to act as the excitation function, and the adjacent convolution combinations are connected by the short circuit layer of the residual network; and the convolutional neural network model is trained, the training data are inputted to the convolutional neural network model and training is performed by using the stochastic gradient descent method, and the last full connection layer is removed out of the trained convolutional neural network model and then only forward propagation is performed so as to act as the face feature data required for face recognition. ReLU + NReLU are used as the excitation function so that the computational burden can be reduced, the accuracy can be guaranteed, the model size can be reduced and the operation speed can be enhanced.

Description

technical field [0001] The present invention relates to the field of face recognition, in particular, to a face recognition method, device and electronic equipment based on deep learning. Background technique [0002] Due to the convenience of the face, face recognition technology has become a hot spot in surveillance, security, finance, social security and other fields. With the help of feature learning of deep learning in recent years, face recognition technology has made great progress. Now many factors such as different lighting, posture, and expression are relatively robust. [0003] In recent years, face detection methods are divided into two categories according to whether deep learning methods are used. Algorithms that do not use deep learning methods have better results, such as joint cascade face detection and align (JDA) and Normalized Pixel Difference (NPD). The JDA method combines face detection and face key point detection, and uses a relatively simple pixel ...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a face recognition method, device and electronic equipment based on deep learning, which is suitable for face feature extraction at the front end, so as to solve the technical problem that the existing model is too large and the speed is slow

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