Feature extraction of finger vein patterns based on iterative line tracking and its application to personal identification

Miura, Naoto; Nagasaka, Akio; Miyatake, Takafumi

doi:10.1002/scj.10596

Cited by 57 publications

(56 citation statements)

References 4 publications

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“…We will demonstrate how to search for the wolf and obtain W AP by applying to a finger-vein-pattern matching algorithm proposed by [4]. This algorithm is similar to an iris-pattern matching algorithm proposed by Daugman [6] 1 .…”

Section: Applying the Wolf Attack To A Finger-vein-patternmentioning

confidence: 99%

“…In this case, we can understand that the wolf attack gains the attack complexity of about 2 59 . Moreover, we will apply the wolf attack to a finger-vein-pattern matching alorithm proposed by [4]. Surprisingly, we show that there exists the wolf which falsely matches any templates for any threshold values.…”

Section: Introductionmentioning

confidence: 99%

“…Section 4 will describe F AR and discuss its limitation as a security measure for the wolf attack. Section 5 will show a result of applying the wolf attack to the finger-vein-pattern matching alorithm proposed by [4]. Section 6 will summarize our results and show future research topics.…”

Section: Introductionmentioning

confidence: 99%

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Wolf Attack Probability: A New Security Measure in Biometric Authentication Systems

Une

Otsuka

Imai

2007

Lecture Notes in Computer Science

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Abstract. This paper will propose a wolf attack probability (W AP ) as a new measure for evaluating security of biometric authentication systems. The wolf attack is an attempt to impersonate a victim by feeding "wolves" into the system to be attacked. The "wolf" means an input value which can be falsely accepted as a match with multiple templates. W AP is defined as a maximum success probability of the wolf attack with one wolf sample. In this paper, we give a rigorous definition of the new security measure which gives strengh estimation of an individual biometric authentication system against impersonation attacks. We show that if one reestimates using our W AP measure, a typical fingerprint algorithm is turned out to be much weaker than theoretically estimated by Ratha et al. Moreover, we apply the wolf attack to a finger-vein-pattern matching algorithm. Surprisingly, we show that there exists an extremely strong wolf which falsely matches all templates for any threshold values.

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Section: Applying the Wolf Attack To A Finger-vein-patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wolf Attack Probability: A New Security Measure in Biometric Authentication Systems

Une

Otsuka

Imai

2007

Lecture Notes in Computer Science

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“…The original image is shown in Fig. 2 (the original image obtained by the Study [6], [7]). The image after ROI extraction is shown in Fig.…”

Section: A Image Acquisition and Region Of Interest Extractionmentioning

confidence: 99%

“…Although, the above algorithm considers the directionality of veins, assumes that the veins oriented in only two principal directions. In [6][7] a method for personal identification based on finger-vein patterns is presented and evaluated using line tracking starting from various positions. This method allows vein patterns to have an arbitrary direction.…”

mentioning

confidence: 99%

A Region Growing Method Based on Statistical Attributes of Infrared Images for Finger Vein Pattern Extraction

Vlachos¹,

Dermatas

2013

BER

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Abstract-In this paper a method for finger vein pattern extraction from infrared images of human finger is proposed. The finger vein pattern is extracted by the execution of a two-step region growing procedure, based on statistical properties of derivatives of the acquired infrared images. Initially, original image is filtered by four different Gaussian kernels (in order to take into account the different orientations of veins). Afterwards, the second partial derivatives of the obtained images are computed. Sequentially, the Hessian matrix of these images is constructed and its eigenvalues are computed in a pixel by pixel basis. The minimum eigenvalue and the absolute value of its gradient comprise the two characteristics (features) used in the two step region growing procedure which follows. The region growing procedure is restricted by statistical attributes such as the mean value and the standard deviation of the segmented regions (vein and tissue) and the mean value and the standard deviation of the gradient of the minimum eigenvalue image. Due to the occurrence of some misclassifications a final post processing step, based on morphological operations, is performed. The developed method achieves to efficiently segment the image despite of intensity variations which are evident in the original image. Moreover, an improved version of the proposed method, which uses the multidirectional response of a specially designed matched filter and its gradient as the two features used in the two stage region growing procedure, is also presented. The modified version, as experimental results show, outperforms the classic version and leads to more robust finger vein pattern extraction.

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Recent Progress of Flexible Image Sensors for Biomedical Applications

2021

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irradiating X-rays, have become indispensable approaches for diagnosing diseases. [4] Other similar applications may include imaging based on positron emission tomography using gamma rays, [10] deep ultraviolet resonant Raman imaging of a cell, [11] histopathology images using visible light, [12] and electroencephalography through nearinfrared (NIR) light [13] (Figure 1).An image sensor, or an imager, is an essential electronic device that supports such type of bioimaging technology. Image sensors have two main components: single or multiple optical sensors (for detecting light) and circuits (for reading the output from optical sensors). Multiple optical sensors are arranged in an array with specific rows and columns. By reading light signals from the sensors using readout circuits, the spatial and/or temporal distribution-localization of biological information is determined, while its dynamics can be analyzed as an image. Two important applications of such bioimaging techniques are static imaging, which involves techniques, such as X-ray and CT, and dynamic imaging, which concerns object detection such as pulse, [14] blood oxygen levels, [15,16] and blood flow. [17] In static imaging, the imaging speed is not required as only little dynamic movements exist, but it requires high resolution and high sensitivity. Meanwhile, in dynamic imaging, high resolution is not required, but high-speed imaging is essential (Figure 2).Recently, the miniaturization of semiconductor devices has led to the development of imaging devices for wearable electronics. As a typical example, a smartwatch integrates small optical sensors that can continuously monitor biological information, such as pulse, blood pressure, and blood oxygen levels, for the long-term by simply wearing the sensors in direct contact with the skin.The flexibility of image sensors is improving to reduce the discomfort caused by such wearable electronics. Because hard and heavy devices cause relatively high discomfort, their prolonged and continuous use increases the psychological and physical burden on the user. Thus, a flexible imaging device that uses soft materials and thin substrates to reduce the device thickness and weight is an effective approach for load reduction. Further, flexible devices are easier to bend and attach to the bioplane than traditional solid devices. Consequently, problems of inaccuracy or intermittency of data acquisition caused by fluctuations in the physical distance between the target skin surface and the device can be reduced. Therefore, flexible imaging Flexible image sensors have attracted increasing attention as new imaging devices owing to their lightness, softness, and bendability. Since light can measure inside information from outside of the body, optical-imaging-based approaches, such as X-rays, are widely used for disease diagnosis in hospitals. Unlike conventional sensors, flexible image sensors are soft and can be directly attached to a curved surface, such as the skin, for continuous measurement of biometric information wi...

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Feature extraction of finger vein patterns based on iterative line tracking and its application to personal identification

Cited by 57 publications

References 4 publications

Wolf Attack Probability: A New Security Measure in Biometric Authentication Systems

Wolf Attack Probability: A New Security Measure in Biometric Authentication Systems

A Region Growing Method Based on Statistical Attributes of Infrared Images for Finger Vein Pattern Extraction

Recent Progress of Flexible Image Sensors for Biomedical Applications

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