Study Of Method Based On Binary Local Feature For Finger Vein Recognition

Finger vein recognition,which is a biometric technique using the vein pattern in finger palmer for human identification,has received the extensive attention and research in recent years.Because the finger vein image is imaged by the infrared light,there are uneven illumination,skin tissue scattering,finger bone occlusion,translation and rotation of finger when imaging,the quality of images in the existing databases are relatively poor.Therefore,there are still many problems that need to be solved in the field of the finger vein recognition.In general,the techniques of finger vein recognition include image acquisition,image restoration,image enhancement,region of interest extraction,feature extraction,image representation,and matching.Among them,feature extraction and image representation are very important for finger vein image recognition.Recently,handcrafted local features(e.g.LBP,LLBP and PLLBP),which are simple,effective,efficient,insensitive to lighting changes and robust to local noise,have been widely studied and successfully applied to finger vein recognition tasks.However,these methods have the following disadvantages:(1)Designing handcrafted local features requires domain knowledge.(2)The range of captured information is limited.Because the larger sampling range used for extracting a local feature,the more information is captured by the local feature,but the dimension of the binary feature will be too long and the storage and computational cost will be increased.(3)Lack of data adaptability.That is to say,the local binary feature extraction for each pixel is performed independently,rather than leaning through training data.(4)Sensitive to the rotation and translation of the finger when acquiring finger vein images.In view of the fact that such binary features are not robust to the rotation and translation of the finger,the bag-of-words framework is introduced into the field of finger vein recognition,which can effectively organize the local features extracted from the images and achieve better recognition results.However,the shortcomings of the handcrafted features have also been inherited.More recently,to overcome the shortcomings of handcrafted local feature,many learning-based local features are proposed in the other field of image recognition and has achieved good recognition results.However,the existing learning-based local feature extraction methods igore the class information of the training image,the manifold structure of original data during the learning process.In addition,few methods pursue the learned binary features with a certain class structure.This thesis focuses on the aforementioned problems and study deeply on the technology of extracting local binary features from finger vein images,and the main work and contributions are as follows:(1)A customized LLBP method for finger vein recognition was studied.Due to the component at different direction of PLLBP has different discrimination for different class,a method for identifying different classes using different directional components is proposed.Through training,the most discriminative components for each class are found.In the testing,the matching score of tesing image is calculated using the most discriminative components,which belong to the class of the template.In this way,not only the storage space of the template is reduced,but also the matching speed is accelerated,and at the same time,the verification performance is improved.(2)A based on supervised learning local feature for finger vein recognition method is proposed.Most of the existing learning-based local binary feature extraction methods do not utilize the class information of image,and the extracted features are lack of discrimination.To overcome this problem,this paper proposes a finger vein local binary feature extraction method based on the surpervised learning.(4)A supervised learning-based personalized finger vein local binary feature extraction method is proposed.Inspired by the LBP feature with bit consistency phenomenon(that is,some bits of the LBP codes belonging to same class have the same value,and the positions combination of these bits belonging to different classes is not the same),the L norm regulation is used to make the learned binary features belonging to each class sparse in row,and linear discriminant analysis is utilized to ensure that the combinations of spared row locations belonging to different classes are different.In this way,the learned binary featues are not only highly discriminative,but also have class structure(the features belonging to a class has a certain structure,and the structures belonging to different class are different).The image level representation using this binary feature by bag-of-words mode is also highly discriminative.In this paper,experiments were performed on two published finger vein databases,and the effectiveness of the proposed method was verified.(3)A based on unsupervised manifold learning local binary feature for finger vein recognition method is proposed.The manifold structure is very important for many machine learning algorithms,such as dimensionality reduction,hashing and clustering.However,the existing learning based local binary feature extraction algorithm rarely uses the manifold structure of the original data.This is because the training data used for local feature learning is big data,the constructing similarity matrix for these big data requires a large storage and computational overhead.To address this problem,this paper proposes a method using an asymmetric graph to appoximited to similarity matrix.This method not only effectively utilizes the manifold of the training data,but also reduces the storage and computational cost,and achieves better results on two public finger vein databases.Considering the complementarity between the featues based on supervised learning and the features based on unsupervised learning,a fusion method at the image representation level is proposed.The fusion method achieves good recognition performance on two public finger vein databases.