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<p>This comprehensive study demonstrates an advanced machine learning framework for distinguishing identical twins using facial skin marks, achieving 96.62% cross-validation accuracy and 90.6% AUC score. The methodology incorporates four distinct hyperparameter optimization techniques (random search, Bayesian optimization, particle swarm optimization, and grid search), comprehensive statistical validation, and a robust preprocessing pipeline including PCA and SMOTE. Analysis of 74 twin pairs from 319 processed images using automated facial mark detection and multi-metric similarity assessment reveals spatial distribution patterns as the primary discriminating factor. The framework employs sophisticated feature engineering (32<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\rightarrow\)</EquationSource> </InlineEquation>15<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\rightarrow\)</EquationSource> </InlineEquation>6 dimensions) and achieves statistically significant performance (<i>p</i>&#xa0;&lt;&#xa0;0.001) with minimal overfitting. Random search optimization emerged as the optimal method, providing the best performance-efficiency trade-off with 90.6% AUC, 88.4% test accuracy, and the fastest execution time (31.8s). The system demonstrates production-ready computational efficiency and establishes a reliable foundation for forensic biometric applications with comprehensive statistical validation and deployment specifications. Figure&#xa0;<InternalRef RefID="Fig1">1</InternalRef> depicts the graphical abstract.</p>

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Facial mark based biometric differentiation of identical twins using dynamic feature enhancement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

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  • Khush Jay Brahmbhatt,
  • Krishna Prakasha,
  • Gangothri Sanil

摘要

This comprehensive study demonstrates an advanced machine learning framework for distinguishing identical twins using facial skin marks, achieving 96.62% cross-validation accuracy and 90.6% AUC score. The methodology incorporates four distinct hyperparameter optimization techniques (random search, Bayesian optimization, particle swarm optimization, and grid search), comprehensive statistical validation, and a robust preprocessing pipeline including PCA and SMOTE. Analysis of 74 twin pairs from 319 processed images using automated facial mark detection and multi-metric similarity assessment reveals spatial distribution patterns as the primary discriminating factor. The framework employs sophisticated feature engineering (32 \(\rightarrow\) 15 \(\rightarrow\) 6 dimensions) and achieves statistically significant performance (p < 0.001) with minimal overfitting. Random search optimization emerged as the optimal method, providing the best performance-efficiency trade-off with 90.6% AUC, 88.4% test accuracy, and the fastest execution time (31.8s). The system demonstrates production-ready computational efficiency and establishes a reliable foundation for forensic biometric applications with comprehensive statistical validation and deployment specifications. Figure 1 depicts the graphical abstract.