Year: 2025
Type: article
Abstract: In the contemporary law enforcement and forensic investigations, the accurate identification of suspects plays a pivotal role in solving crimes and ensuring justice. Traditional methods of suspect identification, such as composite sketches and eyewitness descriptions, often suffer from subjectivity and inconsistency. To address these limitations, there is a growing interest in leveraging advanced technologies, particularly deep learning-based approaches, to enhance the accuracy and reliability of suspect identification processes. This research focuses on the development of a deep learning-based system for generating realistic facial images of potential suspects from textual descriptions. The objective of this project is to develop a deep learningbased system capable of generating realistic facial images of potential suspects based on textual descriptions or other relevant input. The scope of this project encompasses the development and evaluation of a deep learning-based system for generating realistic facial images of potential suspects from textual descriptions within context of criminal investigations. The proposed system aims to provide law enforcement agencies and forensic experts with a more objective and data-driven approach to suspect identification.
Author: Mahanthi Sriramulu
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409378243