Face recognition method based on migration layered network

Abstract

The invention discloses a face recognition method based on a migration layered network. The method comprises the steps of S1, selecting a pre-training model; S2, establishing the layered network for face classification; S3, determining fine-adjustment network layers according to the selected pre-training model and the similar degree of existing face data; S4, for a face training data set, carryingout preprocessing operation, comprising color enhancement, rotation, translation, and addition of random noises; S5, training the existing data through utilization of a caffe depth learning library;and S6, carrying out face recognition through utilization of the trained model.

Description

Technical field [0001] The invention belongs to the field of computer vision, and particularly relates to a face recognition method based on a migration hierarchical network. Background technique [0002] In recent years, deep learning has been widely used in the field of computer vision. Deep learning methods have used more training sets than traditional methods and adopted a deeper network structure, and have also made significant progress in face recognition. However, in some scenarios, due to cost constraints such as resources and manpower, it is impossible to obtain enough labeled training samples for training, resulting in a failure to obtain a better model. Summary of the invention [0003] Purpose of the invention: The present invention proposes a face recognition scheme based on a migration hierarchical network that can be used when there is less face data. Use a trained model with universal significance, and use the weight of this model as the pre-value of the new mode...

AI Technical Summary

Problems solved by technology

However, in some scenarios, due to cost constraints such as resources and manpower, it is impossible to obtain enough labeled training samples for training, resulting in the inability to obtain a better model

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