Abstract 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 15 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.
Brahmbhatt et al. (Mon,) studied this question.