A review on identification of gender using fingerprints

Anjali Mishra; Sweta Jain

doi:10.53730/ijhs.v6nS2.7514

Authors

Anjali Mishra
mishraaj_1@rknec.edu
Student, Department of Computer Science & Engineering,Shri Ramdeobaba College of Engineering and Management, Nagpur, India
Sweta Jain Asst. Professor, Department of Computer Science & Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India

Keywords:

fingerprints, fingerprints impression, biometrics, machine learning, data mining, ridge density, SVM, neural network

Abstract

Every person in the world has unique biometrics characteristics such as iris, face, voice, palm or finger-vein patterns, and fingerprints. Biometrics, such as fingerprints are even more distinctive than DNA. Although identical twins can share DNA, they cannot have identical fingerprints. The fingerprint impressions are created by using ridges and valleys which are present on the surface of fingers. Fingerprints help to afford an infallible means of personal identification because the ridge arrangement on each person's finger is unique and does not change with growth or age. Some studies in machine learning and data mining investigate a relationship between fingerprint and gender. Hereby using ridges present on the finger it can be identified that fingerprint is of male or female, as males have larger body size than females, the equal number of ridges on a larger surface area means males have a lower fingerprint ridge density; finding out the gender from fingerprints can reduce the search space to half. The criminal justice system uses fingerprints to authenticate a convicted offender's identification and track their previous arrests and convictions, criminal tendencies, known associates, and other important information in the absence of DNA.

Downloads

Download data is not yet available.

References

Yong Qi, Menzhe Qiu, Huawei Lin, Jiashu Chen, Yanping Li, Hongguang Lei. "Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender." (Research Square) March 2022: 13

Shima Jalali, Reza Boostani, Mokhtar Mohammadi. "Efficient fingerprint features for gender recognition." (Springer) Aug 2021.

Ahmad Ilham Gustisyaf, Ardiles Sinaga. "Implementation of Convolutional Neural Network to Classification Gender based on Fingerprint." (I.J. Modern Education and Computer Science, 2021, 4, 55-67) Aug 2021.

Ashish Mishra, Shivendra Dubey, Amit Sahu. "Gender Classification Based on Fingerprint Database using Association Rule Mining." Springer Nature Singapore Pte Ltd, 2021: 13.

Ejiogu, Ogechukwu N. Iloanusi & Ugochi C. "Gender classification from fused multi-fingerprint types." (Information Security Journal: A Global Perspective) March 2020.

Kruthi R, Abhijit Patil, Shivanand Gornale. "Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints." (International Journal of Computer Sciences and Engineering) 7, no. 1 (Mar 2019).

P. Gnanasivam, R. Vijayarajan. "Gender classification from fingerprint ridge count and fingertip size." Springer, March 2019: 10.

Mahendra Kanojia, Niketa Gandhi, Leisa J. Armstrong, Chetna Suthar. "Fingerprint Based Gender Identification Using Digital Image Processing and Artificial Neural Network." (Springer International Publishing AG, part of Springer Nature 2018) 2018.

S.S. Gornale, Basavanna M, and Kruthi R. "Fingerprint Based Gender Classification Using Local Binary Pattern." (International Journal of Computational Intelligence Research) 13 (2017)

Liton Devnath, Arindam Kumar Paul, Md. Rafiqul Islam. "A Study on Binary Number of Gender Identification Based on Fingerprints." (International Journal of Scientific & Engineering Research) 7, no. 2 (February 2016).

Eyüp Burak CEYHAN, Şeref SAĞIROĞLU. "Gender Inference within Turkish Population by Using Only Fingerprint Feature Vectors." IEEE, 2015: 5

Acree, Mark A. "Is there a gender difference in fingerprint ridge." (Elsevier) 1999.

Ashish Mishra, Preeti Maheshwary. "A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine." (International Journal of Computer Applications) 169 – No.7 (July- 2017).

Beanbonyka Rim, Junseob Kim & Min Hong. "Fingerprint classification using deep learning approach." (Springer Nature 2020) July 2020.

Eyüp Burak Ceyhan, Şeref Sağıroğlu. "Determining relationship between fingerprint and gender using 10 finger attributes." (SAKARYA UNIVERSITY JOURNAL OF SCIENCE) 2017.

GANESH B. DONGRE, S.M. JAGADE. "Verification of Fingerprint of Transgender with Male and Female." (IRE Journals) 3, no. 4 (Oct 2019).

Javier L, Villalba G, Cicirello V, Hsiao C, Lin C,Wang P, Wu. "Application of Convolutional Neural Network for Fingerprint-Based Prediction of Gender, Finger Position, and Height." 24 (2022).

Mangesh K. Shinde, Prof. S. A. Annadate. "Analysis of Fingerprint Image for Gender Classification or Identification using Wavelet Transform and Singular Value Decomposition." (International Conference on Computing Communication Control and Automation) n.d.

Pattanawit Soanboon, Somsong Nanakorn, Wibhu Kutanan. "Determination of sex difference from fingerprint ridge densityin northeastern Thai teenagers." Egyptian Journal of Forensic Sciences (Elsevier), Sep 2015.

Rhee, W. S. Jeon and S. Y. "Fingerprint pattern classification using convolution neural network." (Internation Journal of Fuzzy Logic and Intelligent System) 17 (2017).

S. F. Abdullah, A. F. N. A. Rahman, Z. A. Abas and W. H. M. Saad. "Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features." (Indian Journal of Science and Technology) 9(9) (March 2016).

Suchita Tarare, Akhil Anjikar, Hemant Turkar. "Fingerprint Based Gender Classification Using DWT Transform." (International Conference on Computing Communication Control and Automation) 2015.

Sudharshan Duth, Megha P Mirashi. "Fingerprint Based Gender Classification using ANN." (International Journal of Engineering and Advanced Technology (IJEAT)) 8, no. 5 (July 2019).

Yadav and Saxena, J Biom Biostat. "A Review on Gender Identification Using Machine Learning Technologies." (Journal of Biometrics & Biostatistics) October 2018.