Finger vein identification using polydirectional local line binary pattern

A Systematic Review of Finger Vein Recognition Techniques

“…In 2010, Lie et al [83] proposed a finger vein verification approach, and obtained accuracy of 97.8% for identification. In term of accuracy, polydirectional local line binary pattern algorithm attained 99.21% accuracy on a dataset of 1902 images [76]. Moreover, in terms of equal error rate, conventional finger vein technique also achieved some tremendous achievements.…”

“…Finger vein images have rich orientation information, and the line patterns obtained only from vertical and horizontal orientation may not have enough discrimination information for matching. To further enhance the discriminatory information, Yu et al proposed polydirectional line pattern (PLLBP) [76] and generalized local line binary pattern (GLLBP) [77] methods, which extract line pattern at an arbitrary orientation. However, LLBP and PLLBP have low discriminatory information and a jumble of redundant information.…”

“…Conventional finger vein approaches were less robust to noise and misalignment than machine learning approaches; therefore, image preprocessing methods were usually applied ahead of feature extraction and matching to overcome the above-mentioned problems. Numerous conventional finger vein identification methods have been developed, but some methods [73,76,78,83] have achieved remarkable development. In 2010, Lie et al [83] proposed a finger vein verification approach, and obtained accuracy of 97.8% for identification.…”

Finger Vein Recognition Using Histogram of Competitive Gabor Responses

“…Although the study of finger vein recognition has a shorter history than fingerprint and face recognition, it has also received remarkable attentions over the last decade [2][3][4][5][6][7][8][9][10][11][12][13]. An enormous volume of literature has been devoted to investigate various feature extraction methods for finger vein recognition, such as repeated line tracking [4], local maximum curvature [5], mean curvature [6], principal component analysis (PCA) with neural network [7], local directional code (LDC) [8], local binary pattern (LBP) [9], polydirectional local line binary pattern (PLLBP) [10], Gabor filter [11,12], and steerable filter [13].…”

“…An enormous volume of literature has been devoted to investigate various feature extraction methods for finger vein recognition, such as repeated line tracking [4], local maximum curvature [5], mean curvature [6], principal component analysis (PCA) with neural network [7], local directional code (LDC) [8], local binary pattern (LBP) [9], polydirectional local line binary pattern (PLLBP) [10], Gabor filter [11,12], and steerable filter [13]. Among these methods, Gabor filter and LBP are the most popular and discriminative local descriptors that have widely used for other biometrics recognition such as iris recognition, face recognition and fingerprint recognition.…”