An identity recognition method based on a dual-channel convolutional neural network

Abstract

The invention discloses an identity recognition method based on a dual-channel convolutional neural network, and the method comprises the steps: carrying out the training of a neural network and the identity recognition, carrying out the comprehensive training and recognition through employing two time synchronization images: a face image and a whole body posture image, avoiding the cheating of asingle factor, and achieving the high anti-interference capability and high recognition accuracy. According to the method, feature data of two channels are connected in a weighted mode through a fullconnection layer, image feature data are obtained through a plurality of convolution layers and a pooling layer, finally the classification probability is obtained through a classifier, and the maximum probability is extracted to be compared with a set threshold value to determine the recognition result. Through multiple times of convolution feature map extraction, nonlinear excitation and poolingdimension reduction processing, the dual-channel convolutional neural network control data is more flexible, and the abstraction capability and the learning capability are stronger, thereby having abetter identification effect.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an identity recognition method based on a dual-channel convolutional neural network. Background technique [0002] With the continuous development of artificial intelligence technology, face recognition technology is becoming more and more mature. Face recognition has been widely used in government, military, bank, Social welfare, e-commerce, security and defense and other fields. However, because the shape of the face is very unstable, people can produce many expressions through changes in the face, and the visual images of the face are also very different from different viewing angles, so the current face recognition technology also has many limitations. In addition, face recognition is also easily affected by various external environments and interference conditions. For example, the collection of portraits is sensitive to the surrounding light environment, an...

AI Technical Summary

Problems solved by technology

However, because the shape of the face is very unstable, people can produce many expressions through changes in the face, and the visual images of the face are also very different from different viewing angles, so the current face recognition technology also has many limitations.

In addition, face recognition is also easily affected by various external environments and interference conditions. For example, the collection of portraits is sensitive to the surrounding light environment, and the difference in light will greatly affect the accuracy of recognition. Factors such as various ornaments and other occluders and face aging also have a greater impact on the recognition rate

Images