sahil485/hand-structure-authentication
Research paper repository for "A Hand Structure-Based Mobile Authentication Solution to the Security-Reliability Trade-off" Paper from NJ Governor's School of Engineering and Technology 2022. The paper proposes a mobile authentication system using hand structure, structure-borne sound, and artificial intelligence
The system extracts hand biometric features from structure-borne acoustic signals captured when mobile devices emit chirp frequencies (18–22 kHz) against the user's hand, using cross-correlation and time/frequency domain feature extraction (MFCC). Classification models (KNN, SVM, Bagged Decision Trees) trained via MATLAB's Classification Learner application differentiate individuals and hand positions, with tunable validation costs to optimize the security-reliability trade-off for specific authentication scenarios. The workflow spans signal generation, recording segmentation, feature calculation, and model training—all implemented in MATLAB with Signal Processing, Statistics and Machine Learning, and Communications toolboxes.
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Jul 30, 2023
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