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Issue title: Special Section: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy, Sushmita Mitra and Ljiljana Trajkovic
Article type: Research Article
Authors: Sivakumar, Trivandrum T.a | Nair, Shali S.a | Zacharias, Geevar C.b | Nair, Madhu S.c; * | Joseph, Anna P.a
Affiliations: [a] Department of Oral Pathology and Microbiology, PMS College of Dental Science and Research, Thiruvananthapuram, Kerala, India | [b] Department of Computer Applications, MES College of Engineering, Kuttippuram, Kerala, India | [c] Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, India
Correspondence: [*] Corresponding author. Madhu S. Nair, Department of Computer Science, University of Kerala, Kariavattom, Thiruvananthapuram, Kerala, India. E-mail: madhu_s_nair2001@yahoo.com.
Abstract: Biometric refers to the automatic identification of a person based on physiological or behavioural characteristics. Current modes of biometric systems are fingerprint, voice, face, signature, palm print, iris scan etc. The conventional biometric systems are unable to meet these authentication requirements as it can be forged. Hence, a novel biometric system which can overcome these limitations is proposed. Tongue is a unique vital organ which is well protected within the mouth and not affected by external factors. Dorsum of the tongue exhibits a great amount of information along with its visual differences in shape, texture and pattern which can be called the tongue print. As tongue exhibits rich textural patterns, Local Binary Pattern (LBP) algorithm is used for extracting features. Extracted features are then trained by a linear Support Vector Machine (SVM) for personal identification. From the database consisting of 136 tongue print images of 34 individuals, we achieved an accuracy of 97.05% for identification. Our study is the first of its kind where texture patterns are extracted from tongue images using Local Binary Pattern for biometric authentication. We achieved a level of accuracy compared to the technique used in other studies.
Keywords: Tongue print, biometric, identification, Local Binary Pattern, Support Vector Machine
DOI: 10.3233/JIFS-169437
Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 3, pp. 1421-1426, 2018
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