A review on gender identification using machine learning based on fingerprints. Issue 5 (4th July 2019)

• Record Type: Journal Article
• Authors: Yadav, Jitendra Singh; Gupta, Amit Kumar; Saxena, Arjit
• Is Part Of: Journal of information & optimization sciences. Volume 40:Issue 5(2019)
• Page Start: 1121
• Page End: 1129
• Publication Date: 2019-07-04
• Subjects: 97R40; Fingerprints -- Epidermal -- Minutiae -- Ridge density -- Fingerprint image -- Classification -- KNN -- PCA -- SVM; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2019.1638002
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: A fingerprint is a unique biometric feature of the individual. It is also known that fingerprints have differences in male and female with respect to ridgeline details. Some studies in machine learning investigate a correlation between fingerprint and gender. In these studies, by analyzing the fingerprint we get important information such as age and gender of a person. Statistical studies have been made in different geographical areas to identify the relationship between fingerprint and gender. This paper illustrates gender classification based on fingerprints through various machine learning techniques like naïve Bayes method, Decision Tree and Support Vector Machine algorithms, KNN, PCA, Wilcoxon-Mann-Whitney Test, Friedman Test. This study introduces the concept of the epidermal ridge, minutiae, ridge areas, ridge density etc. And compare above-stated machine learning techniques, their limitations and strengths based on experimental results for gender classification based on fingerprints. This study can be useful for legislative cases and for researchers to devise new machine learning techniques with improved results.