Small sample classification model construction method based on transfer learning and iris classification application

Abstract

The invention discloses a small sample classification model construction method based on transfer learning and an application of a small sample classification model constructed by the method in iris image classification. The method comprises the steps: constructing an ICP-VGG model based on VGG16 model transfer learning; configuring an activation function of a full connection layer and a dropout ratio of a Dropout layer in the self-defined network according to the iris image task; finely tuning the network, and setting model training related parameters; acquiring a small sample iris data set,and performing data preprocessing and data enhancement on the data set; training and verifying the model, and outputting a recognition result image. According to the method provided by the invention,the deep learning model can be better applied to the field of small sample irises, the over-fitting is reduced, and the recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of computer images, in particular to a method for constructing a small-sample classification model based on transfer learning and an iris image classification application. Background technique [0002] As human beings enter the era of big data and the rapid development of computing equipment, deep learning has entered a period of rapid development. In recent years, relying on the powerful expressive ability of deep learning models and large-scale training data sets, deep learning has achieved remarkable results in the fields of computer vision, speech recognition, etc., especially in the field of image classification, showing an explosive growth. , image classification accuracy on large-scale datasets continues to improve. Large-scale data sets are the cornerstone of deep learning to achieve remarkable results in various fields, but in practical applications, the acquisition of large-scale data sets require...

AI Technical Summary

Problems solved by technology

[0003] In the small-sample iris data set, due to the lack of training data, it is difficult to obtain ideal results by applying the traditional deep learning model, and over-fitting often occurs, that is, the model will perform very well on the training data set, and often the error Will tend to zero, but

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