Handbook Of Vascular Biometrics

[Ronald Frison]

Highquality finger vein datasets available for the research community are still relatively scarce; therefore, we collected a set of finger vein images of high resolution and a known pixel density. Furthermore, this is the first dataset which contains the age, gender and handedness of the participating data subjects as metadata. This dataset has been collected using a custom-designed biometric capture device. The various aspects of designing this biometric capture device are addressed in this chapter. New insights and continuing work on the design of better capture devices have led to novel ideas which are presented in this chapter. To justify the importance of this dataset, performance figures in terms of EER of several well-established algorithms using this dataset and an existing dataset are compared side by side.

The vascular or vein pattern of the finger is advertised as a promising new biometric characteristic. Biometric recognition based on finger vein patterns is characterised by very low error rates, good presentation attack resistance and a user convenience that is equivalent to that of fingerprint recognition. Though this new form of biometrics is already commercially deployed, it still lacks a strong scientific base. This is due to industrial protectiveness, which restricts the ability to verify claimed performances. In order to compare existing algorithms, a standardised testing method is needed and more datasets should be made available to researchers.

In order to stimulate the academic research on vascular pattern recognition, this chapter will present a finger vascular pattern dataset which has recently been made available to other researchers [17]. The presented dataset is unique in its kind as it provides high-resolution images together with demographics about the data subjects. Another contribution of this chapter is the performance verification of several published algorithms using both the newly collected dataset and an existing dataset collected by the Peking University [12].

In the remainder of this chapter, first a brief overview is provided of finger vein acquisition techniques and systems in Sect. 2.2. Next, the custom-designed capture device is described in detail (Sect. 2.3), followed by the dataset (Sect. 2.4). In Sect. 2.5, results of various finger vein recognition algorithms on the database are presented. Section 2.6 presents the next-generation finger vein scanner currently under development at the University of Twente: a more compact design with 3D capabilities and other enhancements. Section 2.7 presents conclusions and in Sect. 2.8 future work is described.

We first briefly summarise the different types of sensors for finger vein recognition and then present our own design. Devices that capture the vascular pattern inside a finger are based on the fact that the haemoglobin inside the veins has a higher absorption of Near-Infrared Light (NIR light) than the surrounding tissue. This means that the vascular pattern inside a finger can be captured by a device that is sensitive to NIR light. The veins have to be made visible with NIR light, but there are multiple possibilities to illuminate the finger. The main types that are found in existing devices are shown in Fig. 2.1.

The illumination with the light reflection method is on the same side as the camera. This allows the device to be more compact. During operation, the user of the device can still see his finger. The disadvantage of this method is that the image sensor mainly captures the reflected light from the surface of the finger, because the light shallowly penetrates the skin. Hence, this method gives images with low contrast between tissue and veins. The light transmission method does deliver high-contrast vascular pattern images, because the light passes through the finger and no reflections of the surface are captured. The illumination is at the other side of the finger relative to the camera. The disadvantage of this method is that the user has to put his finger into the device such that he cannot see his finger anymore, which can cause discomfort. The third illumination type is side lighting method. This method still allows an open device such that the user can see his finger. The light sources are placed on either one side or both sides of the finger. NIR light goes through the sides of the finger and scatters there, before it is captured by the image sensor. This method does allow for high-contrast images. However, the sides of the finger are overexposed in the images.

There are several devices on the market for vascular pattern recognition. The market leader in finger vein capture devices is Hitachi. They have developed multiple systems that are capable of capturing finger vein images using light transmission or side illumination. Hitachi claims that it has a False Non-Match Rate (FNMR) of 0.01% at a False Match Rate (FMR) of 0.0001% [3, 4]. However, it is hard to verify these claims, because the devices and image data are not accessible.

Another company that builds finger vein capture devices is Mofiria, a daughter company of Sony. This company also produces various devices among which one using light transmission, but where the finger is placed sideways on the sensor. They claim an FNMR of 0.1% at an FMR of 0.0001% [15], but again these are closed devices and data are not accessible.

At several universities, research into finger vein recognition is performed and acquisition devices were developed. Examples are the finger vein scanner devices developed by the Civil Aviation University of China [21] and the University of Electronic Science and Technology [9]. The latter device also has the capability of making 3D recordings of finger veins. A more recent sensor, developed at the Norwegian Biometrics Laboratory (NBL), allows simultaneous capturing of both finger vein patterns and fingerprints [13]. This is a closed sensor, and the user has to place his finger through a hole inside the device.

The device developed at the University of Twente, which is described in the subsequent sections, is also an example of this group of finger vein acquisition devices. The huge advantage of these devices, developed by academics, is that they are usually open devices, the image data is accessible and datasets are made available to the research community. This enables us to evaluate and compare various methods for finger vein recognition.

A custom transillumination device type has been designed to capture the finger vascular pattern [18, 19]. This type of capture device has been chosen for its simplicity, robustness and the fact that external light interferences have little influence on the captured images. A downside of this type of capture device is the reduced user convenience because the finger is partially obscured during the capture process. All finger vascular pattern capture devices are based on the fact that blood has a higher absorbency than surrounding tissue in the near-infrared spectrum. A schematic cross section of the capture device can be seen in Fig. 2.2. The USB lightbox is responsible for regulating the individual LED intensities and is encapsulated in the capture device for the ease of portability. The overview also shows the slanted mirror indicated in green and the top plate containing the eight LEDs. The total length of the realised capture device is 50 cm, and the maximum height is 15 cm.

Light source This the most important part of the capture device since it determines the intensity of the captured image. Eight SFH4550 near-infrared LEDs produced by Osram with a wavelength of 850 nm are used to transilluminate the finger. This LED type has been chosen because it has a small angle of half intensity, which means more power can be directed into the finger. Each individual LED intensity is regulated using a simple control loop in such a way that a uniform intensity along the finger is obtained in the captured image. This control loop is also necessary to cope with varying thicknesses along the finger and between various biometric data subjects. The benefit of this simple control loop can be seen in Fig. 2.3. It clearly shows the over- and underexposure in the non-regulated case.

Camera The camera used to capture the images is a BCi5 monochrome CMOS camera with firewire interface produced by C-Cam technologies. The camera has been fitted with a Pentax H1214-M machine vision lens with a focal length of 12 mm. This lens is fitted with a B+W 093 infrared filter which has a cutoff wavelength of 930 nm. The filter is used to block out any interfering visible light. The camera is used in 8-bit mode with a resolution of \(1280 \times 1024\) pixels.

Mirror To minimise the height of the capture device, a mirror is used so the camera can be placed horizontally. An NT41-405 first surface mirror produced by Edmund Optics has been used for this purpose. The reason for choosing a first surface mirror is to avoid distortions in the captured image. A conventional mirror has its reflective layer protected by glass. The refractive indices of glass and air differ which means distortions will occur in the captured image. The final constructed capture device can be seen in Fig. 2.4.

To illustrate and rank the quality of the collected dataset, the performance of a few published algorithms was evaluated. These algorithms have been applied to our collected dataset and the V4 finger vein database from the Peking University [12] which has been used as a reference. The performance of the algorithms is measured in terms of Equal Error Rate (EER). The experiments also investigate the merit of Adaptive Histogram Equalisation (AHE) as a preprocessing step. Each directory of the Peking dataset contains between four and eight images of the same finger. For the experiments only directories containing exactly eight images have been used, this accounts for 153 directories out of the available 200 directories. For this dataset, it is not known which fingers belong to the same subject.

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