LOW DIMENSIONAL REPRESENTATION OF VEIN PATTERNS TO PERFORM
RECOGNITION
Author(s):

Maleika Mehr Nigar Mohamed Heenaye- Mamode Khan, University of Mauritius
Biometric based security systems, which involve the identification of individuals using their
distinct physical or behavioral characteristics, is becoming more popular. Human physiological
features possess the properties such as universality, uniqueness, permanence, collectability and
circumvention. The most popular biometrics are used are fingerprints, iris scans, faces and
signatures Biometric features are harder to copy, forge and very rare for them to be lost. Thus,
biometric offers a more secure and reliable performance [1]. Though many researches have been
carried out on biometric techniques, there seems to be a lot of scope for further improvement of
existing techniques in addition to finding more reliable and practical biometric and techniques
for using them for security purposes. A practical biometric should meet the specified recognition
accuracy, speed and resource requirement, be harmless to users, be accepted by intended
population and be sufficiently robust against various fraudulent methods and attacks to the
system [2].
Recently hand vein pattern biometrics has attracted increasing interest from both research
communities and industries [1]. A vein pattern is the vast network of blood vessels underneath a
person’s skin. The shape of vascular patterns in the same part of the body is believed to be
distinct from each other and remains stable over a long period of time. Furthermore, the blood
vessels are hidden underneath the skin and are mostly invisible to the human eyes and hence vein
patterns are much harder to copy compared to other biometric features. Vein patterns are unique,
stable and have strong immunity to forgery and thus they can become a potential good biometric
which offers secure and reliable features for person identity verification [3].
As a kind of biometric feature authentication system, hand vein recognition has more merits than
the others and thus it has a vast foreground [4]. A typical hand vein pattern biometric consists of
five processing stages [1]: Hand Image Acquisition, image enhancement, vein pattern extraction,
skeletonisation and vein pattern matching. Hand image acquisition consists of capturing the vein
pattern of the dorsal hand. Since the veins are invisible to the naked eye and are found beneath
the skin, we have to use infrared to capture the veins. During experimentation phase, some
researchers found that it is necessary to irradiate the back of the hand using an IR cold source. In
this way, the reduced haemoglobin in veinous blood absorbs more of the incident IR radiation
than the surrounding tissue and thus appearing darker [5]. Image enhancement is the second
phase where noises introduced when capturing images are filtered. In fact, to enhance the quality
of the image, filters are used to reduce the possible imperfections in the image due to sensor
noise and other effects. Different techniques like Gaussian Low Filter, normalization and nonlinear diffusion have been applied [6, 7] to enhance images. The next step is the vein pattern
extraction where the vein patterns are separated from the image background. Thresholding is
used to obtain the vein pattern. Among many other methods, Wang et al [3] have used the idea of
fingerprint biometrics where a fingerprint pattern has been represented by a number of critical
points known as the ridge endings and ridge bifurcations. In the same way, critical points called
minutiae, which are branching and ending points, were extracted from the vein patter. As human
being grows, the vein grows. However, the vein pattern which is the sole feature used to
recognize an individual remains the same. Skeletonisation is then performed to obtain a thinned
version of the vein pattern. The vein pattern is thinned using the thinning algorithms proposed by
Zhang and Suen [8] and the skeleton is obtained. The final phase is vein pattern matching: This
is where the image is compared with the one stored in the database. Correlation and Hausdorff
Distance have been used to match vein patterns [1].
Vein pattern recognition has been so far, done by distinguishing vein patterns by comparing the
properties and relation between pixel values [5, 6, 11]. Consequently, each pixel needs to be
stored and processed, and this makes matching very slow. In this work, we have developed a
method to create a low dimensional representation of vein patterns and then perform recognition.
We have reduced the dimension of representation of veins by using PCA (Principal Component
Analysis). The low dimensional representation of veins in the eigenveins approach is derived by
applying Principal Component Analysis to the set of veins. So far, PCA has been applied to face
recognition and to hand geometry [9, 10]. In the face recognition, the system functions by
projecting face images onto a feature space known as eigenfaces that spans the significant
variations among known face images [9]. We have applied the PCA method successfully to the
dorsal vein patterns. The experimental results obtained are encouraging. The low dimensional
representation has a direct effect on the speed of vein recognition.
References
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