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Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints

Kruti R1 , Abhijit Patil2 , Shivanand Gornale3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 526-533, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.526533

Online published on May 31, 2019

Copyright © Kruti R, Abhijit Patil, Shivanand Gornale . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Kruti R, Abhijit Patil, Shivanand Gornale, “Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.526-533, 2019.

MLA Style Citation: Kruti R, Abhijit Patil, Shivanand Gornale "Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints." International Journal of Computer Sciences and Engineering 7.5 (2019): 526-533.

APA Style Citation: Kruti R, Abhijit Patil, Shivanand Gornale, (2019). Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints. International Journal of Computer Sciences and Engineering, 7(5), 526-533.

BibTex Style Citation:
@article{R_2019,
author = {Kruti R, Abhijit Patil, Shivanand Gornale},
title = {Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {526-533},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4275},
doi = {https://doi.org/10.26438/ijcse/v7i5.526533}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.526533}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4275
TI - Fusion of Features and Synthesis Classifiers for Gender Classification using Fingerprints
T2 - International Journal of Computer Sciences and Engineering
AU - Kruti R, Abhijit Patil, Shivanand Gornale
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 526-533
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

the objective of this work is to study the impact of feature level fusion and synthesis of classifiers for gender classification using fingerprints. Initially, feature level fusion of Multi-Block Projection Profiles features and Segmentation based Fractal Texture Analysis (SFTA) features are extracted for a single instance of fingerprints. Further, along with the feature level fusion and synthesis of classifiers on fingerprint have been piloted and the experiments are conducted accordingly on four different Homologous fingerprint databases. The results reveal that feature level fusion with synthesis of classifiers greatly improves the efficiency of gender classification over the non-fused and single classifier and outperforms the earlier reported techniques.

Key-Words / Index Term

Gender Identification, Biometrics, Fingerprint, SFTA, MBPP, KNN,SVM and Decision Tree classifier

References

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