Multimodal Biometrics Data Based Gender Classification using Machine Vision
Shivanand S Gornale1, Abhijit Patil2, Kruthi R3

1Shivanand S Gornale, Professor, Department of Computer Science, Rani Channamma University, Belagavi, India.
2Abhijit Patil, Research Scholar, Department of Computer Science, Rani Channamma University, Belagavi, India.
3Kruti R, Research Scholar, Department of Computer Science, Jain University, Bangalore, India.

Manuscript received on 26 August 2019. | Revised Manuscript received on 16 September 2019. | Manuscript published on 30 September 2019. | PP: 1356-1363 | Volume-8 Issue-11, September 2019. | Retrieval Number: J96730881019/2019©BEIESP | DOI: 10.35940/ijitee.J9673.0981119
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Abstract: Gender classification from biometrics data is a significant step in forensics to categorize and minimize the suspects search from the criminal records. In this paper, we present multimodal biometrics data analysis for Gender Classification using machine learning algorithms which take input as a Face, Fingerprints and Iris images. Extensive experiments were conducted using feature level and synthesis of classifiers on the SDMULA-HMT and KVK-Multimodal datasets. Experimental results presented using multimodal biometrics data fusion schemes achieves high gender classification accuracies compared to the contemporary techniques stated in the literature.
Keywords: Biometrics, Decision Tree, Fusion, Gender Identification, K NN, Multimodal biometrics, SVM.
Scope of the Article: Classification