Dorsal Hand Vein Recognition Using Gabor Feature-Based 2-Directional 2-Dimensional Principal Component Analysis

Vein Recognition Algorithm Based on Transfer Nonnegative Matrix Factorization

“…The existing researches on vein recognition can be roughly divided into the following two categories: 1) Vein recognition based on gray features. It is very common to extract effective gray features by analyzing the highfrequency information of the vein images, where the highfrequency information can be acquired based on multi-scale analysis theories which include traditional wavelet transform [2][3], Bandelet transform [4], Gabor transform [5][6][7][8][9][10], Curvelet transform [11], Contourlet transformation [12][13], Histogram of Oriented Gridients (HOG) operator [14], spatial curve filtering [15], ridgelet transformation [16], Scale-Invariant Feature Transform (SIFT) [17] and some improved Gabor transform methods [18][19][20][21]; In recent years, some deep learning methods have also been gradually used in vein recognition such as deep neural networks [22] and convolution neural networks [23][24]; In addition, the gray statistical distribution based methods were also verified to be effective such as intensity distribution [25], hierarchical hyper-sphere model [26], sparse representation [27], gradient distribution [28], etc. 2) Vein recognition based on points and curves.…”

Hand Vein Recognition Algorithm Based on NMF with Sparsity and Clustering Property Constraints in Feature Mapping Space

“…In 2004, multiscale theory was first introduced into the gray-based vein feature extraction [3] . Then, many multiscale analysis methods are gradually used, which include 2D wavelet transformation [4,5] , Bandelet transformation [6] , Gabor transformation [7][8][9][10][11][12] and Gabor encoding [13][14][15] , Curvelet transformation [16] , Scale-invariant feature transform(SIFT) [17] , ridgelet transformation [18] , as well as Contourlet transformation [19,20] . 2) Binarybased feature.…”