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Journals / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 9 Issue: 2 Special Issue

Person Verification and Gender Detection Based On Single-hand Image Using Feature Extraction

Zekeriya Katılmış(KUTAHYA HEALTH SCIENCES UNIVERSITY, FACULTY OF ENGINEERING AND NATURAL SCIENCES, DEPARTMENT OF COMPUTER ENGINEERING)0000-0002-2095-5483
Şevval Koç(KUTAHYA HEALTH SCIENCES UNIVERSITY, FACULTY OF ENGINEERING AND NATURAL SCIENCES, DEPARTMENT OF COMPUTER ENGINEERING)0009-0009-6169-0053
Pages
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Publication Date
2026-06-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results show that the SVC and DNN models achieved the highest performance, yielding person verification accuracy of 97.86% and gender detection accuracy of 98.00%, respectively.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Biometric authentication has become increasingly important in security and identity management applications.
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Key Findings & Data:The proposed framework integrates multiple feature extraction and classification strategies within a unified experimental setup.
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Conclusion & Impact:Experimental results show that the SVC and DNN models achieved the highest performance, yielding person verification accuracy of 97.86% and gender detection accuracy of 98.00%, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Biometric authentication · Gender detection · Zernike moments

Abstract

Biometric authentication has become increasingly important in security and identity management applications. This study proposes a multi-stage biometric system capable of performing both person verification and gender detection using single-hand images. The proposed framework integrates multiple feature extraction and classification strategies within a unified experimental setup. Three feature extraction methods (Zernike moments (ZM), wavelet transform (WT), and Hough transform (HT)) are employed in single and hybrid combinations, yielding five distinct feature extraction configurations. The classification stage includes five classical machine learning models, a triple hybrid ensemble model, and one deep learning model, including Decision Trees (DT), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), Support Vector Classifier (SVC), a triple hybrid model (DT+GB+GNB), and a Deep Neural Network (DNN), enabling a comprehensive comparative analysis. Experimental results show that the SVC and DNN models achieved the highest performance, yielding person verification accuracy of 97.86% and gender detection accuracy of 98.00%, respectively. The findings of this study demonstrate the feasibility of a unified hand-based biometric framework and provide valuable insights for the design of robust and scalable biometric authentication systems.

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Cite This Article

Katılmış, Z., Koç, Ş. (2026). Person Verification and Gender Detection Based On Single-hand Image Using Feature Extraction. Sakarya University Journal of Computer and Information Sciences, Volume: 9 Issue: 2 Special Issue. https://doi.org/10.35377/saucis...1807774

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