Abhijit Patil; Kruthi R; Shivanand S. Gornale

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

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Author(s)

Abhijit Patil ^1,* Kruthi R ² Shivanand S. Gornale ¹

1. Department of Computer Science, Rani Channamma University, Belagavi, India

2. Department of Computer Science, Jain University, Bangalore, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.05.04

Received: 9 Feb. 2019 / Revised: 2 Mar. 2019 / Accepted: 21 Mar. 2019 / Published: 8 May 2019

Index Terms

Gender Identification, Biometrics, Multimodal, MB-LBP, BSIF, KNN, SVM

Abstract

A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user’s information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.

Cite This Paper

Abhijit Patil, Kruthi R, Shivanand Gornale, " Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.5, pp. 34-43, 2019. DOI: 10.5815/ijigsp.2019.05.04

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