perimeter. Multi-Scale Line Tracking (MSLT) based
faint edge detection algorithm is used and features
like number of edge pixels (MEP) and number of
Hough votes (MHV ) are used for classification. Ex-
periments are conducted on publicly available IIITD-
Vista, IIITD-Cogent, UND 2010 and our indigenous
database. Results of the experiment indicate that pro-
posed method outperforms previous soft lens detec-
tion techniques.
ACKNOWLEDGEMENTS
Authors would like to thank Indian Institute of Tech-
nology Mandi and IITK for providing funds, intellec-
tual help and guidance.
REFERENCES
Badrinath, G., Nigam, A., and Gupta, P. (2011). An ef-
ficient finger-knuckle-print based recognition system
fusing sift and surf matching scores. In Informa-
tion and Communications Security, volume 7043 of
Lecture Notes in Computer Science, pages 374–387.
Springer Berlin Heidelberg.
Bendale, A., Nigam, A., Prakash, S., and Gupta, P.
(2012). Iris segmentation using improved hough
transform. In Emerging Intelligent Computing Tech-
nology and Applications, volume 304 of Communi-
cations in Computer and Information Science, pages
408–415. Springer Berlin Heidelberg.
Caroline, P. and Andre, M. (2002). The effect of corneal di-
ameter on soft lens fitting, part 2. Contact Lens Spec-
trum, 17(5):56–56.
Details-a (2010). Soft Contact Lens Diameter. Accessed:
2015-5-13.
Details-b (2010). Biometrics Data Sets. http://www3.
nd.edu/∼cvrl/CVRL/Data_Sets.html. Accessed:
2015-06-5.
Erdogan, G. and Ross, A. (2013). Automatic detection of
non-cosmetic soft contact lenses in ocular images. In
SPIE Defense, Security, and Sensing, pages 87120C–
87120C. International Society for Optics and Photon-
ics.
Flom, L. and Safir, A. (1987). Iris recognition system. US
Patent 4,641,349.
Kohli, N., Yadav, D., Vatsa, M., and Singh, R. (2013). Re-
visiting iris recognition with color cosmetic contact
lenses. In Proceedings of International Conference
on Biometrics (ICB), pages 1–7. IEEE.
Kywe, W. W., Yoshida, M., and Murakami, K. (2006).
Contact lens extraction by using thermo-vision. In
18th International Conference on Pattern Recognition
(ICPR), volume 4, pages 570–573. IEEE.
Lovish, Nigam, A., Kumar, B., and Gupta, P. (2015). Ro-
bust contact lens detection using local phase quan-
tization and binary gabor pattern. In 16th Interna-
tional Conference Computer Analysis of Images and
Patterns, CAIP 2015, Valletta, Malta, September 2-4,
pages 702–714.
Nigam, A. and Gupta, P. (2011). Finger knuckleprint based
recognition system using feature tracking. In Bio-
metric Recognition, volume 7098 of Lecture Notes in
Computer Science, pages 125–132. Springer Berlin
Heidelberg.
Nigam, A. and Gupta, P. (2012). Iris recognition using con-
sistent corner optical flow. In 11th Asian Conference
on Computer Vision, Daejeon, Korea, November 5-9,
2012, Revised Selected Papers, Part I, pages 358–369.
Nigam, A. and Gupta, P. (2013a). Multimodal personal
authentication system fusing palmprint and knuck-
leprint. volume 375 of Communications in Computer
and Information Science, pages 188–193.
Nigam, A. and Gupta, P. (2013b). Quality assessment of
knuckleprint biometric images. In 20th International
Conference on Image Processing (ICIP), pages 4205–
4209.
Nigam, A. and Gupta, P. (2014a). Multimodal personal au-
thentication using iris and knuckleprint. In Intelligent
Computing Theory, volume 8588 of Lecture Notes in
Computer Science, pages 819–825. Springer Interna-
tional Publishing.
Nigam, A. and Gupta, P. (2014b). Palmprint recognition
using geometrical and statistical constraints. In 2nd
International Conference on Soft Computing for Prob-
lem Solving (SocProS 2012), December 28-30, 2012,
volume 236 of Advances in Intelligent Systems and
Computing, pages 1303–1315. Springer India.
Nigam, A. and Gupta, P. (2014c). Robust ear recognition
using gradient ordinal relationship pattern. In Com-
puter Vision - ACCV 2014 Workshops - Singapore,
Singapore, November 1-2, 2014, Revised Selected Pa-
pers, Part III, pages 617–632.
Nigam, A. and Gupta, P. (2015). Designing an accurate
hand biometric based authentication system fusing
finger knuckleprint and palmprint. Neurocomputing,
151, Part 3:1120 – 1132.
Nigam, A., T., A., and Gupta, P. (2013). Iris classification
based on its quality. In Intelligent Computing Theo-
ries, volume 7995 of Lecture Notes in Computer Sci-
ence, pages 443–452. Springer Berlin Heidelberg.
Vlachos, M. and Dermatas, E. (2010). Multi-scale retinal
vessel segmentation using line tracking. Computer-
ized Medical Imaging and Graphics, 34(3):213–227.
Yadav, D., Kohli, N., Doyle, J., Singh, R., Vatsa, M., and
Bowyer, K. W. (2014). Unraveling the effect of tex-
tured contact lenses on iris recognition. IEEE Trans-
actions on Information Forensics and Security.
ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods
596