Method for face recognition scene adaptation based on convolutional neural network

Abstract

A method for face recognition scene adaptation based on a convolutional neural network comprises the steps of: 1) collecting face data and making classification tags, performing preprocessing and enhancement of the data, and dividing the data into a training set and a verification set; 2) sending the data in the training set into a designed convolutional neural network for training, and obtaininga pre-training model; 3) testing the pre-training model by employing the data in the verification set, and regulating training parameters to perform retraining according to a test result; 4) repeatedly performing the step 3) to obtain an optimum pre-training model; 5) collecting face image data according to different application scenes, performing fine tuning of the pre-training model on the newlycollected data, and obtaining a new adaption scene model; 6) extracting features of a face image to be tested by employing the adaption scene model, performing weighting operation of the five sense organs of the face in the features, and obtaining final feature vectors; and 7) measuring the final feature vectors by employing a cosine distance, determining whether the face is a target face or not,and outputting a result. The method for face recognition scene adaptation based on the convolutional neural network ensures accuracy of face recognition and scene adaptability of the model.

Description

technical field [0001] The invention relates to the field of face recognition analysis, in particular to a face recognition scene adaptation method based on the combination of convolutional neural network and transfer learning. Background technique [0002] With the rapid development and progress of Internet technology, the demand for technology such as public safety and personal privacy is becoming more and more urgent. In recent decades, the rapid development of biometric identification technology can solve the above problems well. As an intrinsic attribute of the human body, biological characteristics have strong self-stability and uniqueness. At present, biometric identification technologies mainly include face recognition, fingerprint recognition, iris recognition, and voice recognition. Compared with other biometric identification technologies, face recognition technology has the characteristics of easy collection, non-contact, and friendliness, and is easy to be acc...

AI Technical Summary

Problems solved by technology

[0010] 1) Complex features need to be manually designed, which is relatively difficult;

[0011] 2) Poor resistance to interference factors such as light and deformation, and low accuracy

[0016] 1) Poor adaptability to different scenarios;

[0017] 2) When extracting features, operate on the entire face image, and cannot emphasize important parts with large differences such as facial features

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