The Role of Artificial Intelligence in Forensic Biometrics.pptx

The Role of Artificial Intelligence in Forensic

1. The Role of Artificial Intelligence in Forensic Biometrics: Fingerprint, Stamp, and Signature Analysis

2. U nder supervision Dr. Afnan Mohamed Team Ahmed Mohamed ali Yosab ehab dawoud Omar yakout farouk Ahmed usama ahmed Hazem ahmed elsayed Fatma mahmoud Nada mohamed ahmed

3. Table of contents 01 I ntroduction 02 O bjective 03 M ethods 04 R esults 05 C onclusion 06 K eywords

4. I ntroduction 01

5. I ntroduction The field of forensic science is undergoing a significant transformation driven by advancements In Artificial Intelligence (AI). Biometric analysis—the process of identifying individuals based on unique physical or behavioral characteristics—is a cornerstone of forensic investigations. Traditionally, this analysis relied heavily on the meticulous and time-consuming work of human experts examining fingerprints, seals (stamps), and signatures. However, the surge in digital evidence and the need for rapid, scalable analysis have made AI-powered automation not just beneficial but essential. AI algorithms, particularly in deep learning, excel at recognizing complex patterns in high-dimensional data, making them ideally suited for tasks like feature extraction, matching, and anomaly detection in biometrics. This paper explores the application of AI in three key forensic biometric domains: fingerprint identification, historical seal character recognition, and signature verification. It synthesizes findings from recent research to evaluate the efficacy of AI methods while critically arguing for a synergistic approach where AI augments, rather than replaces, human forensic expertise, especially in nuanced cases like signature analysis. !

6. Objective 02

7. Objective The primary objective of this research is to analyze and synthesize the current state of AI applications in forensic biometrics for fingerprints, stamps, and signatures. Specifically, this paper aims to: Evaluate the performance of various AI models (e.g., SIFT, CNN, RNN) in achieving accurate identification and verification. Compare the effectiveness of traditional manual methods against modern digital AI-driven techniques. Highlight the technical challenges and limitations inherent in each biometric modality. Emphasize the critical necessity of human expert oversight in the forensic analysis loop, particularly to interpret AI results and detect sophisticated forgeries where a 100% match may be a red flag.

8. Methods 03

9. Methods The methodologies employed across the reviewed studies leverage a combination of image processing, computer vision, and deep learning techniques, primarily implemented in the Python programming language. Fingerprint Identification: Abdul Razaq et al. (2025) utilized a combination of the Scale-Invariant Feature Transform (SIFT) algorithm for robust feature extraction ( keypoints ) and the Fast Library for Approximate Nearest Neighbors (FLANN) for efficient matching. This combination ensures robustness against changes in orientation and scale while accelerating the search process in large datasets, achieving high accuracy in identifying student fingerprints for access control systems .

10. Seal/Stamp Character Recognition: Sun et al. proposed a Graph Matching-based Chinese Seal Character Recognition (GMCSCR) method. This approach models a seal character as a graph, where nodes represent key points (endpoints, branch points, turning points) and edges represent the strokes connecting them. The system calculates affinity matrices for nodes and edges based on spatial location, shape context, and orientation. A factorized graph matching algorithm then finds the correspondence between two graphs, proving highly effective for recognizing historical seal characters with large intra-class variance and limited training samples. Signature Verification: Two primary approaches were analyzed: Feature-Based Matching (Traditional Digital Methods): Bhadarge & Parkhe (2024) used MATLAB for curve-fitting graphs on signature coordinates and calculating mean image values, and Python for a feature-matching script with a set threshold (e.g., 90%). These methods compare structural features but are less adaptive than deep learning. Deep Learning-Based Recognition: Nguyen (2025) implemented a more sophisticated deep learning pipeline. The process involves extensive image preprocessing ( grayscale conversion, normalization, noise removal, histogram equalization). The core model is a Convolutional Neural Network (CNN) for automatic spatial feature extraction, often combined with a Recurrent Neural Network (RNN) like Long Short-Term Memory (LSTM) to model the temporal dynamics and sequence of the writing stroke. This hybrid CNN-RNN architecture is trained to classify signatures as genuine or forged with high accuracy.

11. Results 04

12. Results The Integration of AI has yielded significantly improved results in forensic biometric analysis compared to traditional methods: Fingerprint Identification: The SIFT+FLANN method demonstrated 100% accuracy across various rotations (0°, 90°, 180°, 270°) in tests, proving its invariance to orientation changes. The processing was also efficient, with feature extraction and matching times averaging between 11-20 ms per image . Seal Character Recognition: The graph-matching method (GMCSCR) significantly outperformed standard classifiers like SVM, CNN, and SRC on a dataset of 751 historical seal characters, achieving a top-1 accuracy of 83.42% and a top-5 accuracy of 91.33%. This highlights its superiority in scenarios with limited and imbalanced training data where deep learning models may struggle .

13. Signature Verification: The results clearly demonstrate the advantage of deep learning : The traditional digital methods (MATLAB and Python feature-based) achieved a high accuracy of 95-96% but required manual parameter tuning. The deep learning-based approach (CNN-LSTM) achieved the highest performance, with accuracy, precision, recall, and F1-score all reaching ~96% after 50 epochs of training. Data augmentation and early stopping were crucial strategies to prevent overfitting and enhance generalization . A critical result, particularly from the signature analysis research, is the understanding that a 100% match is not only improbable but is often a strong indicator of forgery. Natural human variation ensures that no two genuine signatures are absolutely identical. A perfect match suggests a traced or copied signature, lacking the natural fluctuations present in authentic human writing. This is a prime example of where AI output must be interpreted by a human expert; the AI can flag a high-confidence match, but the human must determine if that "perfection" is biologically credible or forensically suspicious.

14. Conclusion 05

15. Conclusion Artificial Intelligence has undeniably revolutionized forensic biometrics, bringing unprecedented speed, accuracy, and automation to the analysis of fingerprints, seals, and signatures. Techniques like SIFT+FLANN, graph matching, and deep CNN-RNN models have proven highly effective in processing complex biometric data, often surpassing the capabilities of traditional manual analysis in terms of efficiency and scalability. However, this research concludes that AI is not a standalone solution. The role of the human expert remains paramount. AI systems can process millions of data points and identify patterns invisible to the human eye, but they lack the contextual understanding, reasoning ability, and domain experience of a trained forensic analyst. This is most evident in signature verification, where a 100% match is a paradox—it is a definitive sign of forgery, a nuance an AI might miss without human guidance. The analyst must interpret the AI's results, validate Its findings against other evidence, and make the final judicial determination.

16. Therefore , the future of forensic biometrics lies not in choosing between human expertise and AI, but in fostering a collaborative synergy. AI should be viewed as a powerful tool that augments the capabilities of human experts, handling large-scale data processing and initial screening, while humans provide the critical judgment, interpret complex cases, and uphold the ethical and legal standards required in forensic investigation. The most robust systems will be those that seamlessly integrate artificial intelligence with human intelligence

17. Keywords 06

18. Artificial Intelligence Forensic Biometrics Fingerprint Recognition Seal/Stamp Analysis Signature Verification Deep Learning Convolutional Neural Networks (CNN ) Human-AI Collaboration. Keywords