Face image feature extraction and comparison method based on deep learning

Abstract

The invention discloses a face image feature extraction and comparison method based on deep learning. The method comprises the steps: performing local modularization on the images in a face library; constructing a convolution neural network, and inputting image modules into the convolution neural network to train; obtaining the image modules after performing local modularization on the images in a face library, using the trained convolution neural network model to extract the features of each image module, and obtaining the feature vector corresponding to each image; after performing local modularization on the face images to be compared, utilizing the trained convolution neural network model to extract the features of each image module, and obtaining the feature vectors corresponding to the face images to be compared; and using the cosine similarity to compare the feature vectors corresponding to the face images to be compared with the feature vector corresponding to each image in order. For the face image feature extraction and comparison method based on deep learning, face comparison in the OFD eastern face library ranks top 5, and the accuracy is 95%.

Description

technical field [0001] The invention relates to the field of information technology, in particular to a method for extracting and comparing features of human face pictures based on deep learning. Background technique [0002] Face comparison is dedicated to identifying whether two faces belong to the same person. The difficulty lies in whether stable features can be obtained. Early commonly used face feature extraction algorithms such as: LBP, Gabor, haar, etc., but the accuracy rate is affected by environmental factors and there is a bottleneck. In recent years, more and more deep models, especially convolutional neural networks (CNN), have been used to extract deep visual features. The convolutional neural network model can obtain more abstract and essential visual features. However, there are still face images affected by various factors such as illumination, expression, posture, makeup, etc., which bring great interference to the extraction of facial image features, th...

AI Technical Summary

Problems solved by technology

However, there are still face images affected by various factors such as illumination, expression, posture, makeup, etc., which bring great interference to the extraction of facial image features, thus greatly reducing the efficiency of recognition.

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