Sparse-regularization-based face recognition method capable of realizing multiband face image information fusion

Abstract

The invention relates to a sparse-regularization-based face recognition method capable of realizing multiband face image information fusion and belongs to the technical field of image processing. The method comprises the following steps of: 1, acquiring visible-light, near-infrared, intermediate-infrared, far-infrared and thermal-infrared training images; 2, performing normalization, background removal and illumination preprocessing; 3, extracting visible-light, near-infrared, intermediate-infrared, far-infrared and thermal-infrared sample facial features; 4, selecting the sample facial features based on a sparse regularization method, evaluating coefficient and endowing weight under the classification significance according to the sample facial features, and fusing the sample facial features to obtain feature vectors representing the samples to serve as index vectors; 5, forming a feature set according to the index vector; 6, segmenting a facial part from an image to be tested, performing operation of the step 2 to step 4 on the facial part to be tested to obtain a feature vector of the facial part to be tested; and 7, sequentially calculating the distance between the feature vector of the facial part to be tested and the feature vectors of the feature set, and selecting the sample which corresponds to the feature set with the minimal distance and is the person of the image to be tested. The method has the advantages that change of accessories, shelter and the like is overcome; the recognition precision is high; and the application range is wide.

Description

technical field [0001] The present invention relates to a method in the technical field of image processing, in particular to a face recognition method that combines visible light, near-infrared, mid-infrared, far-infrared and thermal-infrared face information through a sparse regularization algorithm. Background technique [0002] Face recognition technology uses a computer to obtain face images and perform analysis and preprocessing, then extracts features that can effectively represent face images in a specific way, and finally uses machine learning to identify face images. Face recognition is widely used in human-computer interaction systems, security verification systems, verification of driver's licenses and passports, and identification of criminals. With the development of information and network technology in recent years, face recognition has become one of the most concerned issues in the field of pattern recognition. [0003] One of the most important research to...

AI Technical Summary

Problems solved by technology

The features fused by this type of fusion method are generally relatively single, and the feature dimension is relatively high.

[0004] Although multi-band images other than visible light have good robustness to changes in illumination intensity, there are still many defects, such as the loss of some texture features during imaging, which makes it difficult to adapt to changes in expression and posture.

Although visible light images are sensitive to illumination changes, they have strong robustness in these aspects.

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