A Review on Gender Identification Using Machine Learning Technologies based on Fingerprints

Journal of Biometrics & Biostatistics

article

YadavJS¹  SaxenaA¹ 

[1] Department of Computer Science, JECRC University

关键词: Fingerprints;    Epidermal;    Minutiae;    Ridge density;    Valleys;    Frequency domain analysis;    Gender identification;    Fingerprint image;    Classification;    KNN;    PCA;    SVM;   

DOI  :  10.4172/2155-6180.1000412

来源: Hilaris Publisher

【 摘 要 】

Fingerprint is a unique biometric feature of individual. It is also known that fingerprints have differences in male and female with respect to ridge line details. Some studies in machine learning investigate a relationship 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 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.

【 授权许可】

Unknown