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Year 2018, Volume: 2 Issue: 1, 29 - 38, 30.06.2018

Abstract

References

  • [1] N.E. Fayrouz, N. Farida, A.H. Irshad, “Relation between fingerprints and different blood groups”, J Forensic leg Med., 19(1), 18-21, 2012.
  • [2] S. Selvarani, S. Jebapriya, R.S. Mary, “Automatic Identification and Detection of Altered Fingerprints”, International Conference on Intelligent Computing Applications (ICICA), Coimbatore, 239-243, 2014.
  • [3] A. Gyaourova, A. Ross, “A Novel Coding Scheme for Indexing Fingerprint Patterns”, Structural, Syntactic, and Statistical Pattern Recognition, 5342, 755-764, 2008.
  • [4] I.S. Msiza, B. Leke-Betechuoh, F.V. Nelwamondo, N. Msimang, “A fingerprint pattern classification approach based on the coordinate geometry of singularities”, IEEE International Conference on Systems, Man and Cybernetics, San Antonio, Texas, USA, 510-517, 2009.
  • [5] S. Nanakorn, P. Poosankam, A. Nanakorn, “An Application of Automated Inkless Fingerprint Imaging Software in Fingerprint Collection and Pattern Analysis”, Second International Conference on Innovative Computing, Information and Control, Kumamoto, Japan, 53, 2007.
  • [6] D. Bhavana, J. Ruchi, T. Prakash, J.L. Kalyan, “Study of Fingerprint Patterns In Relationship with Blood Group and Gender- a Statistical Review”, Research Journal of Forensic Sciences, 1(1), 15-17, 2013.
  • [7] A. Bharadwaja, P.K. Saraswat, S.K. Agrawal, P. Banerji, S. Bharadwaj, “Pattern of fingerprints in different ABO blood groups”, Journal of Forensic medicine & Toxicology, 21(2), 49-52, 2004.
  • [8] S.K. Raloti, K.A. Shah, V.C. Patel, A.K. Menat, R.N. Mori, N.K. Chaudhari, “An Effort to Determine Blood Group and Gender From Pattern of Finger Prints”, Natl J Community Med., 4(1), 158-160, 2013.
  • [9] M.A. Soman, R. Avadhani, M. Jacob, R. Nallathamby, “Study of Fingerprint Patterns in Relationship with Blood Group and Gender”, International Journal of Current Research, 5(12), 3994-3997, 2013.
  • [10] P. Rastogi, K.R. Pillai, “A study of Fingerprints in Relation to Gender and Blood Group”, Journal of Indian Academy of Forensic Medicine, 32(1), 11-14, 2010.
  • [11] M.R. Sangam, A.R. Babu, K. Krupadanam, K. Anasuya, “Fingerprint Patterns In different Blood Groups”, J Indian Acad Forensic Med., 33(4), 343-345, 2011.
  • [12] Y.N. Umraniya, H.H. Modi, H.K. Prajapati, “Study of Correlation of Finger Print Patterns in Different ABO, Rh Blood Groups”, International Journal of Scientific Research, 2(9), 337-339, 2013.
  • [13] D.E.O. Eboh, “Fingerprint patterns in relation to gender and blood group among students of Delta State University, Abraka, Nigeria”, Journal of Experimental and Clinical Anatomy, 12(2), 82-86, 2013.
  • [14] A.U. Ekanem, H. Abubakar, N.I. Dibal, “A Study of Fingerprints in Relation to Gender and Blood Group among Residents of Maiduguri, Nigeria”, IOSR Journal of Dental and Medical Sciences, 13(8), 18-20, 2014.
  • [15] A.A. Mehta, A.A. Mehta, “Palmar Dermatoglyphics in ABO, Rh Blood Groups”, International Journal of Biological & Medical Research, 2(4), 961-964, 2011.
  • [16] A. Koneru, S. Hunasgi, P. Sinha, R. Surekha, M. Vanishree, S. Ravikumar, “Association of different finger prints in relation to ABO and Rh blood groups”, International Journal of Biological & Medical Research, 5(3), 4287-4292, 2014.
  • [17] R.K. Arora, D. Badal, “Subject Distribution Using Data Mining”, International Journal of Research in Engineering and Technology, 2(12), 219-222, 2013.
  • [18] T.R. Patil, S.S. Sherekar, “Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification”, International Journal of Computer Science and Applications, 6(2), 256-261, 2013.
  • [19] A. Cufoglu, M. Lohi, K. Madani, “Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) - comparative study”, International Conference on Computer Engineering & Systems, Cairo, Egypt, 210-215, 2008.
  • [20] S.B. Aher, L.M.R.J. Lobo, “Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning”, International Journal of Computer Applications, 41(6), 1-10, 2012.

Analysis of Blood Groups from Fingerprint Patterns of Turkish Citizens

Year 2018, Volume: 2 Issue: 1, 29 - 38, 30.06.2018

Abstract

This study examines 10 fingerprints of each of 82 Turkish citizens the ages between 18 and 70 and presents the results of blood group distribution of people from only fingerprints. The fingerprint patterns are first divided into 3 categories such as loop, whorl and arches. The blood group distribution was then analyzed according to these categories.  It was found that the loop-type patterns were found more intensively than other patterns, AB blood group had little relation among loop-type patterns and loop rate for A blood group is higher. The results were compared to other types of patterns.  The results were also demonstrated that the relation among fingerprint feature vectors and blood groups might exist and blood group can be achieved from fingerprints. It is expected that the proposed analysis might help to develop systems and various new applications such as determination of blood group of a criminal from a fingerprint found on crime scene and determination of blood group of a person when he/she borns fastly and without any cost. 

References

  • [1] N.E. Fayrouz, N. Farida, A.H. Irshad, “Relation between fingerprints and different blood groups”, J Forensic leg Med., 19(1), 18-21, 2012.
  • [2] S. Selvarani, S. Jebapriya, R.S. Mary, “Automatic Identification and Detection of Altered Fingerprints”, International Conference on Intelligent Computing Applications (ICICA), Coimbatore, 239-243, 2014.
  • [3] A. Gyaourova, A. Ross, “A Novel Coding Scheme for Indexing Fingerprint Patterns”, Structural, Syntactic, and Statistical Pattern Recognition, 5342, 755-764, 2008.
  • [4] I.S. Msiza, B. Leke-Betechuoh, F.V. Nelwamondo, N. Msimang, “A fingerprint pattern classification approach based on the coordinate geometry of singularities”, IEEE International Conference on Systems, Man and Cybernetics, San Antonio, Texas, USA, 510-517, 2009.
  • [5] S. Nanakorn, P. Poosankam, A. Nanakorn, “An Application of Automated Inkless Fingerprint Imaging Software in Fingerprint Collection and Pattern Analysis”, Second International Conference on Innovative Computing, Information and Control, Kumamoto, Japan, 53, 2007.
  • [6] D. Bhavana, J. Ruchi, T. Prakash, J.L. Kalyan, “Study of Fingerprint Patterns In Relationship with Blood Group and Gender- a Statistical Review”, Research Journal of Forensic Sciences, 1(1), 15-17, 2013.
  • [7] A. Bharadwaja, P.K. Saraswat, S.K. Agrawal, P. Banerji, S. Bharadwaj, “Pattern of fingerprints in different ABO blood groups”, Journal of Forensic medicine & Toxicology, 21(2), 49-52, 2004.
  • [8] S.K. Raloti, K.A. Shah, V.C. Patel, A.K. Menat, R.N. Mori, N.K. Chaudhari, “An Effort to Determine Blood Group and Gender From Pattern of Finger Prints”, Natl J Community Med., 4(1), 158-160, 2013.
  • [9] M.A. Soman, R. Avadhani, M. Jacob, R. Nallathamby, “Study of Fingerprint Patterns in Relationship with Blood Group and Gender”, International Journal of Current Research, 5(12), 3994-3997, 2013.
  • [10] P. Rastogi, K.R. Pillai, “A study of Fingerprints in Relation to Gender and Blood Group”, Journal of Indian Academy of Forensic Medicine, 32(1), 11-14, 2010.
  • [11] M.R. Sangam, A.R. Babu, K. Krupadanam, K. Anasuya, “Fingerprint Patterns In different Blood Groups”, J Indian Acad Forensic Med., 33(4), 343-345, 2011.
  • [12] Y.N. Umraniya, H.H. Modi, H.K. Prajapati, “Study of Correlation of Finger Print Patterns in Different ABO, Rh Blood Groups”, International Journal of Scientific Research, 2(9), 337-339, 2013.
  • [13] D.E.O. Eboh, “Fingerprint patterns in relation to gender and blood group among students of Delta State University, Abraka, Nigeria”, Journal of Experimental and Clinical Anatomy, 12(2), 82-86, 2013.
  • [14] A.U. Ekanem, H. Abubakar, N.I. Dibal, “A Study of Fingerprints in Relation to Gender and Blood Group among Residents of Maiduguri, Nigeria”, IOSR Journal of Dental and Medical Sciences, 13(8), 18-20, 2014.
  • [15] A.A. Mehta, A.A. Mehta, “Palmar Dermatoglyphics in ABO, Rh Blood Groups”, International Journal of Biological & Medical Research, 2(4), 961-964, 2011.
  • [16] A. Koneru, S. Hunasgi, P. Sinha, R. Surekha, M. Vanishree, S. Ravikumar, “Association of different finger prints in relation to ABO and Rh blood groups”, International Journal of Biological & Medical Research, 5(3), 4287-4292, 2014.
  • [17] R.K. Arora, D. Badal, “Subject Distribution Using Data Mining”, International Journal of Research in Engineering and Technology, 2(12), 219-222, 2013.
  • [18] T.R. Patil, S.S. Sherekar, “Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification”, International Journal of Computer Science and Applications, 6(2), 256-261, 2013.
  • [19] A. Cufoglu, M. Lohi, K. Madani, “Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules (LBR) and Instance-Based Learner (IB1) - comparative study”, International Conference on Computer Engineering & Systems, Cairo, Egypt, 210-215, 2008.
  • [20] S.B. Aher, L.M.R.J. Lobo, “Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning”, International Journal of Computer Applications, 41(6), 1-10, 2012.
There are 20 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Eyüp Burak Ceyhan

Merve Gullu

Ceren Ulucan This is me

Publication Date June 30, 2018
Published in Issue Year 2018 Volume: 2 Issue: 1

Cite

APA Ceyhan, E. B., Gullu, M., & Ulucan, C. (2018). Analysis of Blood Groups from Fingerprint Patterns of Turkish Citizens. Journal of Engineering and Technology, 2(1), 29-38.