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Behavioral Biometric Data Analysis for Gender Classification Using Feature Fusion and Machine Learning.

Shivanand S Gornale¹, Sathish Kumar¹, Abhijit Patil¹

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

Frontiers in Robotics and AI

|May 24, 2021

Summary

This summary is machine-generated.

This study introduces a new method to determine gender from handwritten signatures using textural and statistical features. The algorithm achieved high accuracy, showing potential for document authentication and forensic analysis.

Keywords:

HOG K-NN LBP biometrics decision tree gender classification offline handwritten signature support vector machine

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Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

Area of Science:

Computer Science
Biometrics
Forensic Science

Background:

Biometric security is crucial for access control.
Handwritten signatures are widely accepted for document authentication.
Behavioral biometrics offer unique identification traits.

Purpose of the Study:

To propose a novel algorithm for gender detection from handwritten signature images.
To fuse textural and statistical features for improved classification accuracy.
To enhance security and forensic analysis of documents.

Main Methods:

Extraction of Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features for texture analysis.
Utilizing machine learning classifiers including k-Nearest Neighbors (k-NN), decision trees, and Support Vector Machines (SVM).
Evaluation on a custom dataset of 4,790 handwritten signatures.

Main Results:

Achieved high accuracy rates: 96.17% for k-NN, 98.72% for decision tree, and 100% for SVM.
Demonstrated the effectiveness of fusing textural and statistical features.
Validated the proposed algorithm on a substantial dataset.

Conclusions:

The proposed signature-based gender detection method is highly accurate and reliable.
This technique can significantly aid in computer vision tools for document authentication.
The algorithm shows promise for forensic investigations involving handwritten documents.