Time-Varying Change Tracking of Fingernail Biometrics Based on MATLAB and Origin Software

Objectives: This work explores the 90-day temporal variation of fingernail biometric features and compares the performance of MATLAB and Origin software to analyze these changes. The study is primarily set to determine the temporal stability of fingernail biometrics and the efficacy of computational tools in monitoring and analyzing these changes. Method: A case study design was employed, capturing fingernail photographs at three time points: Day 1, Day 8, and Day 90. Feature extraction and classification were performed using a support vector machine (SVM) in MATLAB, while contour mapping and histogram analysis were conducted using Origin software. The accuracy of classification, standard deviation of the pixel intensity, and contour complexity were measured to quantify biometric changes. Findings: The experiment found the deterioration of classification performance with time due to physiological change. The performance of the MATLAB model reduced from 98.5% on Day 1 to 92.1% on Day 8 and 84.3% on Day 90, impacting biometric recognition. Origin contour mapping identified higher structural complexity with 18.4% and 35.7% rise in Day 8 contour and Day 90 contour density, respectively. Histogram analysis produced a 50.2% increase in standard deviation on the 90th day, indicating variation in biometric parameters. MATLAB was able to pick these up but was reset, and Origin was used for demonstration purposes but not for predictive modeling. Novelty: This study addresses time-varying changes in fingernail biometrics against the time-invariance hypothesis. It implies vulnerabilities in traditional systems and encourages hybrid machine learning and statistical visualization-based approaches for improved accuracy. Keywords: Biometric Identification, Fingernail Characteristics, Machine Learning, Biometric Changes, MATLAB, Origin Software