Palm vein recognition scheme based on an adaptive Gabor filter

Ma, Xin; Jing, Xiaojun; Huang, Hai; Cui, Yuanhao; Mu, Junsheng

doi:10.1049/iet-bmt.2016.0085

Cited by 43 publications

(38 citation statements)

References 23 publications

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“…For the vein-based biometrics systems, there is another recognition method that has been used for identification/recognition, called palm-vein recognition method. This recognition method is similar to finger-vein recognition by the use of vein structure inside a hand for recognition task [ 11 , 12 , 51 , 52 ]. Similar to finger-vein recognition systems, the palm-vein recognition systems are also vulnerable to attackers [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Nguyen

Yoon

Pham

et al. 2017

Sensors

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Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

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Section: Resultsmentioning

confidence: 99%

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Nguyen

Yoon

Pham

et al. 2017

Sensors

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“…This aims to enhance the quality of the images with low contrast. The technique successfully increases the dynamic range of the wrist [32]. Next, resizing image process is done.…”

Section: B Overview Our Purposed Methodsmentioning

confidence: 99%

Local Descriptor Approach to Wrist Vein Recognition with DVH-LBP Domain Feature Selection Scheme

Fronitasari¹,

Basari²,

Gunawan³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Local Binary Pattern (LBP) is one of the well-known image recognition descriptors for texture-based images due to its superiority. LBP can represent texture well due to its ability to discriminate and compute efficiency. However, when it is used to describe textures that are barely visible, such as vein images (especially contactless vein), its discrimination ability is reduced, which leads to lower performance. LBP has extensively been implemented for features extraction in recognition system of hand, eye, face, eye, and other images. Nowadays, there are a lot of developments of hand recognition systems as a hand is a part of the body that can be easily used in the recognition process and it is easier to contact the sensor when taking the image (user-friendly). In particular, a hand consists of various parts that can be used, such as palm and fingers. Other parts like dorsal and wrist can also be used as they have unique characteristics, i.e., they are different from each other, and they do not change with ages. Changes in pixel intensity can be derived from skeletal vein images to distinguish individuals in palm vein recognition. In the previous paper, we proposed a method diagonal, vertical, horizontal local binary pattern (DVH-LBP) for implementing the palm vein recognition system successfully. Through this work, we improve our previous procedure and implement the improved method for recognizing wrist. In particular, this study proposes a new and robust directional extraction technique for encoding the functions of the wrist vein in a simple representation of binary numbers. Simulation results show the low equal error rate (ERR) of the proposed technique is 0.012, and the recognition rate is 99.4%.

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“…Another approach proposed by Kang et al [16], makes use of a local invariant feature extraction technique based on the square root of the scale invariant feature transform (RootSIFT). The work of Ma et al [21] presents an adaptive bidimensional Gabor filter for feature extraction, which are compared using the minimum normalised Hamming distance method, whilst Ahmad et al [2] make use of the wave atom transform (WAT) method for feature extraction and the normalised Hamming distance to compute the matching score. Hong et al [9] make use of a hierarchical classifier based on the fusion of the block dominant orientation code (BDOC) and block-based histogram of oriented gradient (BHOG) features from different spectrum bands (red, green, blue and NIR).…”

Section: Related Workmentioning

confidence: 99%