Infrared face identification method based on local parallel nerve network

Abstract

The invention discloses an infrared face identification method based on a local parallel nerve network. A network structure mainly comprises four portions and the method is characterized by 1, extracting a preliminary convolution characteristic: through one group of 2*2 convolution kernel, extracting a preliminary face characteristic and arranging an output characteristic signal; 2, generating a parallel multi-scale convolution characteristic: using a parallel multi-scale convolution network structure to extract face characteristics representing different scale information; 3, generating a classification characteristic vector: using a fully-connected layer to integrate the convolution characteristic so as to acquire a characteristic vector which is used to finally represent a face identity and is used for classification input, and carrying out modified linear activation and random neglect processing; and 4, training and testing a classifier: inputting a processed fully-connected characteristic vector into a Softmax classifier and calculating losses, and reversely spreading, training and adjusting a network parameter so as to realize infrared face identification. The method can be widely applied to infrared face identification and identity identification applications.

Description

technical field [0001] The invention relates to an infrared face recognition method based on a local parallel neural network, which belongs to the technical fields of digital image processing, pattern recognition and infrared engineering. It mainly involves deep neural network and multi-scale feature classification technology, which can be widely used in applications such as infrared face recognition and identity verification. Background technique [0002] Infrared face recognition technology has been developing as an important technology for infrared image processing and pattern recognition. Although face recognition technology for visible light images has matured, under some extreme conditions that do not have visible light face imaging, based on The face recognition system of medium and long-wave infrared sensors has become a very important information processing approach, so infrared face recognition has important research and application value. Different from visible l...

AI Technical Summary

Problems solved by technology

[0003] In the infrared face image, it is easier to distinguish the temperature distribution between the foreground and the background of the face. However, due to the high similarity of the temperature distribution between the faces of different identities, and the fact that the infrared image does not have texture The details make identification very difficult, so it is difficult for traditional feature-based descriptors and classifiers to achieve high recognition accuracy

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