A fast retrieval method for fine-grained face images based on deep learning

Abstract

The invention provides a fast retrieval method for fine-grained face images based on deep learning, comprising: constructing a deep convolutional neural network model, and adding a loss layer for calculating a loss function to each branch layer of the deep convolutional neural network model; Initialize the parameters of the deep convolutional neural network model; construct a data set, and randomly divide the pictures in the data set into a training set, a test set, and a verification set according to a preset ratio; set the parameters of the deep convolutional neural network model Learning parameters; the deep convolutional neural network model is trained, and the parameters of the deep convolutional neural network model are updated using stochastic gradient descent and backpropagation algorithm; the trained deep convolutional neural network model is tested, specifically after Coarse-grained testing and fine-grained testing are used to obtain face retrieval results, thereby improving the efficiency and accuracy of image retrieval.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to a fast retrieval method for fine-grained face images based on deep learning. Background technique [0002] With the convenience of collecting images by mobile phones and surveillance cameras and the popularity of the Internet such as Weibo and WeChat, the scale of data has grown explosively. These data greatly increase the storage and calculation burden of computing equipment. Taking face retrieval as an example, we first need to extract real-valued features such as GIST features, LBP features, and CNN features from millions of data. Secondly, use the Euclidean distance or the inner product as the similarity distance measure, and sort the pictures in the database according to the similarity with the query picture. Storing these real-valued features requires a lot of computer memory, and calculating the Euclidean distance or inner product distance between these real-valu...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the technical problems of low efficiency and poor retrieval accuracy in image retrieval in the prior art, the present invention provides a fast retrieval method for fine-grained face images based on deep learning, thereby improving the efficiency of image retrieval and improving the efficiency of image retrieval. precision

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