Enhanced Criminal Identification Using Integrated Face Recognition and Blockchain Technology
DOI:
https://doi.org/10.69996/jsihs.2025010Keywords:
Blockchain, Face Recognition, Criminal Identification, Biometric Security, Data Integrity, Fraud DetectionAbstract
This paper presents a novel Blockchain-Based Face Detection and Recognition System aimed at enhancing criminal identification through the integration of deep learning algorithms and blockchain technology. The system leverages an Integrated Deep Learning (IDL) model for high-accuracy facial recognition under varying conditions such as occlusion, lighting variations, and background noise. To ensure data integrity, transparency, and immutability, every recognition result is securely recorded on a blockchain ledger, preventing unauthorized access and tampering. The proposed framework addresses key challenges in conventional surveillance systems, such as centralized vulnerabilities and data manipulation, by providing a decentralized, tamper-resistant architecture. Experimental results demonstrate high performance in terms of accuracy, precision, recall, and F1-score, even in challenging environments The proposed model was tested under diverse real-world conditions, including frontal faces, occlusion, side angles, lighting variations, and spoofing attempts. The system achieved a maximum recognition accuracy of 99.1%, with precision reaching 98.7%, recall at 99.3%, and an F1-score of 99.0% during real criminal identification scenarios. Even in challenging environments, such as low lighting and background noise, the model maintained accuracy above 89%, demonstrating robustness. Spoof detection was successfully handled with 98.3% accuracy and only 1.4% false positive rate, showcasing the system's resilience against fraudulent attempts. Each recognition event is securely logged on a blockchain ledger, ensuring data immutability and traceability, with an average logging time of under 480 milliseconds.
References
[1] M. A. N. U. Ghani, K. She, M. A. Rauf, S. Khan, J A Khan et al., “Enhancing security and privacy in distributed face recognition systems through blockchain and GAN technologies,” Computers, Materials and Continua, vol.79, no.2, pp.2609-2623, 2024.
[2] S. H. G. Salem, A. Y. Hassan, M. S. Moustafa and M.N. Hassan, “Blockchain-based biometric identity management,” Cluster Computing, vol.27, no.3, pp.3741-3752, 2024.
[3] M. A. N. U. Ghani, K. She, M. A. Rauf, S. Khan, J A Khan et al., “Toward robust and privacyenhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning,” Mathematical Biosciences and Engineering, vol.21, no.3, pp.4165-4186, 2024.
[4] M. A. N. U. Ghani, K. She, M. A. Rauf, S. Khan, J A Khan et al., “Securing synthetic faces: A GANblockchain approach to privacy-enhanced facial recognition,” Journal of King Saud UniversityComputer and Information Sciences, vol.36, no.4, pp.102036, 2024.
[5] R. Kumar, P.V. Jambur, N. V. Kulkarni, N. S.R. Kapini, N. RN et al., “Securing ML Models on Websites: Face Recognition and Spoof Detection via IPFS Blockchain,” In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), pp. 1-8, 2025.
[6] G. Uteyev and R.F. Gibadullin, “Development of the Decentralized Biometric Identity Verification System Using Blockchain Technology and Computer Vision,” In 2024 International Russian Smart Industry Conference (SmartIndustryCon), pp. 350-355, 2024.
[7] A.A. Ahmed and O. Alabi, “Secure and scalable blockchain-based federated learning for cryptocurrency fraud detection: A systematic review,” IEEE Access, 2024.
[8] R. Suganya, P. Ashok, S. Shanmuga Priya and K.Somasundaram, “Criminal Identification Based On Database Management Using Blockchain,” In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), pp. 1-5, 2024.
[9] S. Aanjanadevi, S. Aanjankumar, K.R. Ramela and V. Palanisamy, “Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics,” Computer Systems Science and Engineering, vol.46, no.1, 2023.
[10] M.R.I. Sattar, M. T. B. H. Efty, T.S. Rafa, T. Das, M.S. Samad et al., “An advanced and secure framework for conducting online examination using blockchain method,” Cyber Security and Applications, vol.1, ppp.100005, 2023.
[11] V.T. Truong and L.B. Le, “Security for the metaverse: Blockchain and machine learning techniques for intrusion detection,” IEEE Network, vol.38, no.5, pp.204-212, 2024.
[12] S. U. Abas, F. Duran and A. Tekerek, “A Raspberry Pi based blockchain application on IoT security,” Expert Systems with Applications, vol.229, pp.120486, 2023.
[13] A. Yazdinejad, A. Dehghantanha, R.M. Parizi, G. Srivastava and H. Karimipour, “Secure intelligent fuzzy blockchain framework: Effective threat detection in iot networks,” Computers in Industry, vol.144, pp.103801, 2023.
[14] N. A. Alsharif, S. Mishra and M. Alshehri, “IDS in IoT using Machine Learning and Blockchain,” Engineering, Technology and Applied Science Research, vol.13, no.4, pp.11197-11203, 2023.
[15] V. M. Rathod and O.P. Sharma, “Performance enhancement of deep learning-based face detection system,” In Artificial Intelligence, Blockchain, Computing and Security, vol.2, pp. 53-59, 2023.
[16] Z. Liu and X. Li, “Sok: Security analysis of blockchain-based cryptocurrency,” arXiv preprint arXiv:2503.22156, 2025.
[17] D. Chen, Z. Liao, R.Chen, H. Wang, C. Yu et al., “Privacy-preserving anomaly detection of encrypted smart contract for blockchain-based data trading,” IEEE Transactions on Dependable and Secure Computing, vol.21, no.5, pp.4510-4525, 2024.
[18] H. Farooq, A. Altaf, F. Iqbal, J.C. Galán, D.G. Aray and I. Ashraf, “DrunkChain: blockchain-based IoT system for preventing drunk driving-related traffic accidents,” Sensors, vol.23, no.12, pp.5388, 2023.
[19] M. Rostami, A. Farajollahi and H. Parvin, “Deep learning-based face detection and recognition on drones,” Journal of Ambient Intelligence and Humanized Computing, vol.15, no.1, pp.373-387, 2024.
[20] V. Mukesh, “A Comprehensive Review of Advanced Machine Learning Techniques for Enhancing Cybersecurity in Blockchain Networks,” Journal ID, vol.8736, pp.2145, 2025.
[21] M. Hajian Berenjestanaki, H.R. Barzegar, N. El Ioini and C. Pahl, “Blockchain-based e-voting systems: a technology review,” Electronics, vol.13, no.1, pp.17, 2023.
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