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Finger Vein Representation by Modified Binary Tree Model
Tong Liu,Jianbin Xie,Huanzhang Lu,Wei Yan,Peiqin Li 한국산학기술학회 2013 SmartCR Vol.3 No.2
Finger vein recognition has high identification accuracy and strong security performance, which can be used in banks, offices, factories, etc. Although image representation is not a necessary process for finger vein recognition, a proper representation method can help to explore distribution regularities and structure differences of finger veins, and provides instructive information for finger vein recognition. It is very difficult to represent finger veins because of their irregular structure. Therefore, four principles (caliber uniformity, node replication, loop splitting, and virtual connection) are proposed in this paper, first to simplify the finger vein structure as a binary tree structure. Then a modified binary tree model is proposed based on the binary tree structure. The new model uses the binary tree to describe the relationships between different vein branches and uses a B-spline function to describe the spatial structure of vein branches. Experiments show that this model can quantitatively describe the relationships between, and the spatial structure of, vein branches with little representation error and low storage space requirements.
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( Huiwu Luo ),( Fei Zhao ),( Shangfeng Chen ),( Huanzhang Lu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.3
Bag of visual words is a popular model in human action recognition, but usually suffers from loss of spatial and temporal configuration information of local features, and large quantization error in its feature coding procedure. In this paper, to overcome the two deficiencies, we combine sparse coding with spatio-temporal pyramid for human action recognition, and regard this method as the baseline. More importantly, which is also the focus of this paper, we find that there is a hierarchical structure in feature vector constructed by the baseline method. To exploit the hierarchical structure information for better recognition accuracy, we propose a tree regularized classifier to convey the hierarchical structure information. The main contributions of this paper can be summarized as: first, we introduce a tree regularized classifier to encode the hierarchical structure information in feature vector for human action recognition. Second, we present an optimization algorithm to learn the parameters of the proposed classifier. Third, the performance of the proposed classifier is evaluated on YouTube, Hollywood2, and UCF50 datasets, the experimental results show that the proposed tree regularized classifier obtains better performance than SVM and other popular classifiers, and achieves promising results on the three datasets.
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