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\(\rightarrow\)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.
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Data availability
The dataset used in this study, the ND-TWINS-2009-2010 database, is owned by a third party (the University of Notre Dame) and is subject to a specific license agreement. Therefore, the authors do not have the authority to share or distribute this data. Researchers interested in accessing the dataset must contact the original curators directly to obtain a license. Information on acquiring the dataset is available from the University of Notre Dame’s Computer Vision Research Laboratory (CVRL). Access to the ND-Twins-2009-2010 dataset requires a license agreement authorized by the university. The dataset can be requested through the following link: ND-Twins-2009-2010: [Online Access] - https://cvrl.nd.edu/projects/data/#nd-twins-2009-2010.
Abbreviations
- AAM:
-
Active appearance models
- AUC:
-
Area under the curve
- CNN:
-
Convolutional neural networks
- CV:
-
Cross-validation
- DNA:
-
Deoxyribonucleic acid
- FERET:
-
Facial recognition technology
- FPR:
-
False positive rate
- FRST:
-
Fast radial symmetry transform
- GLCM:
-
Gray level co-occurrence matrix
- Hog:
-
Histogram oriented of gradients
- LBP:
-
Local binary pattern
- LoG:
-
Laplacian-of-gaussian
- MBE:
-
Multi-biometric evaluation
- MRF:
-
Markov random field
- ND TWINS:
-
Notre dame-twins
- PCA:
-
Principal component analysis
- PR:
-
Precision-recall
- PSO:
-
Particle swarm optimization
- ROC:
-
Receiver operating characteristic
- RNN:
-
Recurrent neural networks
- SIFT:
-
Scale invariant feature transform
- SMOTE:
-
Synthetic minority oversampling technique
- SMT:
-
Scars, marks and tattoos
- SURF:
-
Speeded up robust features
- ORB:
-
Oriented FAST and rotated BRIEF
- TAR:
-
True accept rate
- TPR:
-
True positive rate
- XGBoost:
-
eXtreme gradient boosting
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Acknowledgements
The authors would like to thank the University of Notre Dame, United States (UND), for sharing the ND-TWINS-2009–2010 Dataset.
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Open access funding provided by Manipal Academy of Higher Education, Manipal. The author(s) declare that no agency has funded this research.
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Mr. Khush Jay Brahmbhatt: Writing—Original draft preparation; Dr. Gangothri Sanil: Writing—Reviewing and Editing; Dr. Krishna Prakasha: Supervision; All authors have read and agreed to the published version of the manuscript.
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This study involved the analysis of a third-party, controlled-access dataset: the ND-TWINS-2009-2010 database, collected and curated by the University of Notre Dame (UND). The original data collection was conducted with ethical approval and informed consent from participants and/or their legal guardians, as detailed by the data source. Our research team’s use of the dataset was performed in strict accordance with a signed license agreement with UND, which governs data privacy and security protocols. This agreement is attached for the editor’s reference.
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Brahmbhatt, K.J., Prakasha, K. & Sanil, G. Facial mark based biometric differentiation of identical twins using dynamic feature enhancement. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39470-y
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DOI: https://doi.org/10.1038/s41598-026-39470-y
