Image feature extraction method, device, terminal equipment and system

Abstract

The invention provides an image feature extraction method, which comprises the steps of: firstly, receiving an image input by a user; then registering the image input by the user to obtain a registered image; constructing a plurality of structured sub-images of the registered image; adopting a feature model obtained through multi-model training for extracting visual features of each structured sub-image; subjecting the visual features of the plurality of structured sub-images to structured fusion to obtain structured feature data; and finally, adopting a model obtained through structured model training for calculating the structured feature data, so as to obtain image feature data. Compared with the prior art, the image feature extraction method has the advantages that the obtained image feature data is a feature vector, since the feature vector and the model keep structured features of the image in the training process, the image feature data has higher accuracy and identifiability, and can have higher accuracy when being applied to image identification, particularly to face identification, thereby acquiring a better identification effect.

Description

technical field [0001] The present application relates to the field of electronic technology, and specifically relates to an image feature extraction method, an image feature extraction device, an image feature extraction terminal device, and an image feature extraction system. Background technique [0002] Face recognition research began in the 1990s. At the beginning, there were eigen face method to describe face with image principal components and fisher face method to describe face image with distinguishing features; after entering this century, based on LBP Gabor's face local feature description method and boosting-based discriminative feature learning method have quickly become the mainstream; in recent years, with the introduction of deep learning (deep learning) method, face recognition technology has been pushed to a new level. steps. At present, the representative cutting-edge technologies in the field of face recognition are as follows: [0003] First of all, th...

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Problems solved by technology

[0005] The research group of Professor Tang Xiaoou from the Chinese University of Hong Kong conducted a more in-depth study on the face recognition technology based on deep learning. They used multiple face sub-images to train the deep neural network separately, and then connected the features output by each sub-neural network in series. A better recognition effect is obtained, but this simple concatenation of the features extracted from each sub-image loses the structural characteristics of the image itself

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