Face recognition method based on aggregate loss deep metric learning

Abstract

The invention discloses a face recognition method based on aggregate loss deep metric learning. The method comprises the steps of 1), preprocessing a training image; 2), performing pre-training on a deep convolutional neural network by means of the preprocessed image, using a softmax loss as a loss function and introducing key point pooling technology; 3), inputting all training images into a pre-trained model, and calculating the initial kind center of each kind; 4), performing fine adjustment on the pre-trained model by means of the aggregate loss, aggregating the samples of the same kind to the kind center through iteratively updating a network parameter and the kind center, and simultaneously increasing distances between different kind centers, thereby learning robust discriminative face characteristic expression; and 5), in application, performing preprocessing on the input image, and respectively inputting the input image into the trained network model for extracting characteristic expression, and realizing face recognition through calculating similarity between different faces. The face recognition method can realize relatively high face recognition accuracy just through training small mass of data.

Description

technical field [0001] The invention relates to the technical fields of biological feature recognition, computer vision and deep learning, and in particular to a face recognition method based on aggregation loss depth metric learning. Background technique [0002] At present, with the rapid development of Internet technology, information security is seriously threatened, but on the other hand, it also makes information security issues more and more attention. Identity recognition is an important embodiment of information security and plays an important role in practical applications. Compared with traditional identification technology, biometric identification technology has the characteristics of uniqueness, persistence, security, universality and practicability. Face recognition has been widely used due to its intuition, non-contact, ease of use and many other advantages. [0003] Face recognition is one of the most challenging topics in the field of computer vision and ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a face recognition method based on aggregation loss depth metric learning. This method utilizes the idea of ​​clustering centers to make similar samples gather toward the class center, and different classes The center is kept far away, and the key point pooling technology is introduced at the same time, which can make full use of the face structure information, effectively reduce the computational complexity, and still have a high recognition accuracy in the case of fewer training samples

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