Intelligent Iris Analysis Framework for Identity Recognition and Investigation

Authors

  • Alpita Narayan Scholar, B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Shreya Verma Scholar, B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Anu Chaudhary Scholar, B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Priyanshu Awasthi Scholar, B. Tech Final Year, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author
  • Dr. Nikhat Akhtar Associate Professor, Department of Computer Science & Engineering, Goel Institute of Technology & Management, Lucknow, Uttar Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT25113102

Keywords:

Biometrics, Iris Recognition, Security System, Image Processing, Pattern Recognition, Image Acquisition

Abstract

Biometrics relates to the identification of individuals by their distinctive physical traits. It may rely on facial recognition, iris patterns, fingerprints, and DNA analysis. This study introduces IRIS (Intelligent Recognition & Investigation System), a sophisticated, AI-driven forensic analytical instrument intended to transform crime scene investigation. IRIS offers law enforcement agencies a comprehensive solution for evidence analysis and crime-solving by integrating advanced technologies such as deep learning-based age detection, human scream detection, handwriting analysis with forgery detection, and crime scene object detection. The system utilizes convolutional neural networks, support vector machines, and natural language processing methods to process and analyse many forms of evidence. Our assessment indicates that IRIS attains elevated accuracy levels throughout its constituent modules: 92% precision in classification tests, coupled with effective real-time processing capabilities accommodating up to 1000 simultaneous users without notable performance decline. This study advances the expanding domain of AI applications in forensic science and illustrates the capacity of intelligent systems to improve the efficiency and efficacy of criminal investigations.

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Published

11-05-2025

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Research Articles