10.1117/2.1200903.1539
Personal identification using
finger knuckles
Ajay Kumar
A novel biometrics system employing finger-knuckle-surface measurements has achieved promising, highly accurate results.
Developing secure and effective access-control systems requires
personal-identification technologies that are reliable and convenient. Hand-based biometrics exploits several internal and external features that are quite distinct in a given individual. User
acceptance of hand-based biometrics systems is very high, and
they are becoming more convenient and user friendly with the
introduction of peg-free and touchless imaging.
The anatomy of the human hand is quite complicated. The
finger-back surface—the ‘dorsum’ of the hand—can be very useful in user identification, but it has not yet attracted significant attention of researchers. In particular, the image-pattern
formation from finger-knuckle bending is highly unique and
thus makes this surface a distinctive biometric identifier. The
anatomy of our fingers allows them to bend forward and
resist backward motion. This asymmetry results in a very
limited number of creases and wrinkles on their palmside.
Therefore, finger-knuckle patterns are a promising avenue for
further developments in touchless personal identification.1
The advantages of employing finger-knuckle imaging are
numerous. First, user acceptance of outer-palm surface imaging
is very high since, unlike for fingerprints, there is no stigma of
potential criminal investigation associated with this approach.
Second, the finger geometry can be acquired simultaneously
from the same image and employed to improve the system’s performance. Peg-free imaging of the finger-back surface is also convenient. Such images can be acquired online and used to extract
scale, translational, and rotational-invariant knuckle features for
reliable identification.
We have developed a completely automated system that
recognizes individuals using finger-knuckle images (see Figure
1). The system uses a machine-vision camera and automatically
segments the relevant regions to extract meaningful information (see Figure 2). A large fraction of Indian users wear rings,
signifying faith, religious belief, good health, or long-term
Figure 1. Automated extraction of finger knuckles for identification.
relationships. The system has therefore been designed to automatically identify the presence of rings and extract the most
suitable region for finger identification.
Texture analysis of the normalized knuckle-image regions
can reveal highly discriminative information for identification
purposes. Analysis of the acquired knuckle images in both the
spatial and frequency domains has also been explored. Twodimensional Gabor filters are appropriate for this purpose:2
they have tunable angular- and axial-frequency bandwidths and
center frequencies, and achieve optimal joint resolution in both
the spatial and frequency domains. Phase information can be
extracted from knuckle creases and lines using comparative
responses from the even and odd components of the Gabor filters and used to form a ‘KnuckleCode,’ similar to IrisCode2
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10.1117/2.1200903.1539 Page 2/2
Author Information
Ajay Kumar
Biometrics Research Laboratory
Department of Electrical Engineering
Indian Institute of Technology Delhi
New Delhi, India
Ajay Kumar is an assistant professor and the founder of the Biometrics Research Laboratory. His research focuses on biometrics
and computer-vision-based industrial inspection.
References
Figure 2. Live display and hand recognition.
or FingerCode.3 Alternatively, the dominant orientations of
Gabor filters in a filter bank can also be used to extract phase
information.4 Personal identification using such a KnuckleCode
yields promising results that are comparable to or better than
several other current biometric approaches.1, 5 A comparative
performance study using individual knuckle images from the
five fingers of one hand suggests that middle and ring fingers
yield highly discriminant information and achieve the best performance compared to the thumb, index, or little finger.
The performance of finger-knuckle identification depends
sensitively on the accuracy of knuckle segmentation from
the fingers or hands being measured. Therefore, further
performance improvement can be achieved by developing
more accurate knuckle-segmentation schemes. This can also be
achieved through some tradeoff in user convenience by employing pegs during imaging (as done in some earlier versions of
hand-geometry or palmprint systems).
In summary, the first online personal-identification system
employing finger-knuckle surface measurements has achieved
promising results and an accuracy comparable to or better than
other hand-based biometrics systems. However, efforts to employ traditional texture-phase information using knuckle lines
and creases are not yet satisfactory and further efforts are required to investigate the performance of knuckle biometrics for
potential application to a large user population.
1. A. Kumar and C. Ravikanth, Personal authentication using finger knuckle surface,
IEEE Trans. Inform. Forens. Security. In press.
2. J. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. 15, pp. 1148–1161, 1993.
3. A. K. Jain, S. Prabhakar, L. Hong, and S. Pankanti, Filterbank-based fingerprint
matching, IEEE Trans. Image Process. 9, pp. 846–859, 2000.
4. W. K. Kong and D. Zhang, Competitive coding scheme for palmprint verification,
Proc. Int’l Conf. Pattern Recogn., pp. 520–523, 2004.
5. A. Kumar, Personal identification using finger knuckle imaging, IITD Techn. Rep.
IITD-BRL-07-2, 2007.
c 2009 SPIE