Face Verification Method Based on Convolutional Neural Network and Bayesian Decision

Abstract

The invention discloses a face verification method based on convolutional neural network and Bayesian decision-making. The steps are as follows: 1) training convolutional neural network and Bayesian model with human face training database; Preprocessing such as face detection and alignment, randomly combining test faces into 6000 pairs of faces; 3) Extracting feature vectors of test face image pairs with convolutional neural network, and calculating similarity; 4) Dimensionality reduction of feature vectors by PCA Then send it into the Bayesian network, calculate the posterior probability combined with the similarity, set the threshold and determine whether each pair of faces belongs to the same person. The invention enhances the robustness of face authentication, improves the speed and accuracy of face authentication, and can be used in fields such as identity authentication and public safety.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a face verification method based on convolutional neural network and Bayesian decision-making in the technical field of pattern recognition and artificial intelligence. The present invention uses the image feature information obtained from the convolutional neural network to announce a new face verification system through the method of Bayesian decision-making, which includes face detection, face preprocessing, face feature extraction and human face verification. Face authentication and other content have enhanced the robustness of face authentication and improved the speed and accuracy of face authentication. It can be applied to fields such as identity authentication and public security, and can improve the accuracy and efficiency of image processing. Background technique [0002] With the continuous development of face recognition technology, face images are mo...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that since this method is an authentication method based on unified subspace analysis, its real-time performance cannot be guaranteed. Once the natural background is complex and the face pattern is changeable, its authentication accuracy will not be guaranteed. also can not get satisfactory results

However, the shortcomings of this method are that since this method is a face authentication method based on neural network learning, although its recognition effect is relatively ideal and its self-learning ability is strong, but for face authentication, its intra-class discrimination is far away. Less than the between-class discrimination, the intra-class discrimination needs to be improved

Images