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

Abstract

The invention discloses a facial feature recognition method and system based on the multi-region characteristic and metric learning. The method comprises the steps that convolution neural network parameters of the corresponding location and scale are obtained through the multi-scale facial area training, and corresponding facial area features are extracted according to the neural network parameters; the above features are filtered to obtain the high dimensional facial features; metric learning is conducted according to the high dimensional facial features, the after defined loss function of the feature expression is obtained through the dimension reduction processing of the features, a network model of the metric learning is obtained through the training of the loss function; the images to be recognized are inputted into the network model, the facial features are dimension reduced using the Euclidean distance in order to be recognized. In the method, multiscale is used to select multiple areas, the convolutional neural networks are trained, and the expression skills of the characteristics are improved. Meanwhile, through the selection of the acquired multi-scale features, the efficiency of expression of characteristics 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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