Research Article

AI-Powered Forensic Face Drawing: An Overview of the Research on Methodological, Theoretical, and Real-World Defects in Suspect Identification Systems

Authors

Kannan Nagarajan * Department of Artificial Intelligence and Data Science, Sri Shakthi Institute of Engineering and Technology, Coimbatore-641062, Anna University, Chennai.
Jeeva Sakthivel Department of Artificial Intelligence and Data Science, Sri Shakthi Institute of Engineering and Technology, Coimbatore-641062, Anna University, Chennai.
Rosenpranav Kogilavani Satheeshkumar Department of Artificial Intelligence and Data Science, Sri Shakthi Institute of Engineering and Technology, Coimbatore-641062, Anna University, Chennai.
Mohan Raja Velusamy Department of Artificial Intelligence and Data Science, Sri Shakthi Institute of Engineering and Technology, Coimbatore-641062, Anna University, Chennai.
Kamalaveni Vanjigounder Department of Artificial Intelligence and Data Science, Sri Shakthi Institute of Engineering and Technology, Coimbatore-641062, Anna University, Chennai.

https://doi.org/10.48314/ceti.vi.54

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Abstract

Artificial Intelligence (AI) has transformed forensic facial sketching, introducing advanced deep learning architectures for suspect identification in constrained-data environments. This literature survey systematically analyzes the state of the art in AI-driven forensic facial sketching, identifying critical gaps across methodological, theoretical, and practical dimensions. Methodologically, we highlight the lack of comparative studies across deep learning architectures (e.g., GANs, VAEs, diffusion models), the over-reliance on accuracy as the sole evaluation metric, and the insufficient investigation of algorithmic robustness to noise and distortions. Theoretically, we identify gaps in understanding how AI models interpret and reconstruct facial features from sparse witness descriptions, as well as in the limited research on inference mechanisms with incomplete data. Practically, we note deficiencies in real-world scenario testing, user-centric design for forensic practitioners, and system scalability for operational deployment. By synthesizing existing literature, this survey not only identifies these interconnected gaps but also proposes future research directions to develop more robust, efficient, and forensically applicable AI systems. Our analysis emphasizes the need for standardized benchmarks, comprehensive evaluation protocols, and interdisciplinary collaboration to advance the field.

Keywords

Forensic facial sketching, Deep learning architectures, Generative adversarial networks, Suspect identification, Evaluation metrics

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Published 2025-01-27
Issue Vol. 2 No. 1 (2025): Computational Engineering and Technology Innovations
Section Articles
Document Type Research Article
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How to Cite

Nagarajan, K., Sakthivel, J., Satheeshkumar, R. K., Velusamy, M. R., & Vanjigounder, K. (2025). AI-Powered Forensic Face Drawing: An Overview of the Research on Methodological, Theoretical, and Real-World Defects in Suspect Identification Systems. Computational Engineering and Technology Innovations, 2(1), 11-22. https://doi.org/10.48314/ceti.vi.54

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