Face authentication method based on convolutional neural network and Bayesian decision

Abstract

The invention discloses a face authentication method based on a convolutional neural network and Bayesian decision. The face authentication method comprises the steps of 1), training the convolutional neural network and a Bayesian model by means of a face training database; 2), performing preprocessing such as face detection and face alignment on a testing database, and randomly combining test faces for obtaining 6000 pairs of faces; 3), extracting a characteristic vector of a testing face image pair by means of the convolutional neural network, and calculating similarity; and 4) after performing PCA dimension reduction on the characteristic vector, feeding the characteristic vector into a Bayesian network, calculating posterior probability according to the similarity, setting a threshold and determining whether each pair of faces belongs to one person. The face authentication method has advantages of improving robustness in face authentication and improving face authentication speed and face authentication accuracy. The face authentication method can be used in the field of identity authentication, public security, etc.

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