Facial feature recognition method and system based on multi-region feature and metric learning

Abstract

The invention discloses a face feature recognition method and system based on multi-region feature and metric learning. The method includes: obtaining convolutional neural network parameters of corresponding positions and scales through multi-scale face region training, and according to the convolutional neural network The parameters extract the features of the corresponding area of ​​the face; filter the above features to obtain high-dimensional face features; perform metric learning according to the high-dimensional face features, perform dimensionality reduction processing on the features to obtain feature expressions, and then define a loss function. The loss function training is used to obtain a network model of metric learning; after the image to be recognized is input into the network model, the face features are reduced in dimension and then recognized by Euclidean distance. In the present invention, multi-scale selection of multi-regions is used to train the convolutional neural network, which improves the expressive ability of features. At the same time, by selecting the acquired multi-scale features, the expression efficiency of the features is improved, and the accuracy of face recognition is effectively improved.

Description

technical field [0001] The invention relates to the field of image recognition and processing, in particular to a face feature recognition method and system based on multi-region feature and metric learning. Background technique [0002] Face recognition technology is based on human facial features. For the input face image or video stream, it first judges whether there is a face, and if there is a face, it further gives the position, size and each face of each face. Location information of major facial organs. Based on this information, the identity features contained in each face are further extracted, and compared with known faces, so as to identify the identity of each face. Face recognition technology consists of three parts: 1) face detection, 2) face tracking, and 3) face comparison. [0003] In the existing face recognition technology, one implementation method is to extract face features + Euclidean distance recognition through single-scale / area training of the fa...

AI Technical Summary

Problems solved by technology

[0003] In the existing face recognition technology, one implementation method is to extract face features + Euclidean distance recognition through single-scale / area training of the face area, but the disadvantage is that the expression ability of the extracted features is limited, and the accuracy of face recognition Low

Another way is to extract face features through face multi-region training + principal component analysis (PCA) dimensionality reduction + joint Bayesian (Joint-Bayesian) method, but its disadvantage is that the recognition speed is slow

Another way is to extract face features from multiple areas of the face + Euclidean distance or cosine distance recognition, but its disadvantage is that the feature dimension is high and the storage space is large.

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