Deep learning model for digital forensics face sketch synthesis

In the realm of digital forensics, the importance of face sketch synthesis cannot be overstated, as it plays a pivotal role in the precise identification of suspects, thereby significantly enhancing the overall investigative processes. This specialized technique involves the creation of facial sketches based on available information or descriptions, contributing crucial visual data that aids law enforcement agencies in their efforts to solve crimes and apprehend individuals involved. Face sketch synthesis proves particularly valuable in cases where traditional methods may fall short, such as instances with limited or no surveillance footage. By harnessing digital technology and artistic interpretation, forensic artists or software can generate facial sketches that help law enforcement agencies build a clearer profile of potential suspects. thereby contributing to advancements in forensic science and supporting law enforcement efforts. This particular domain has been propelled into a new era by the fusion of face sketch synthesis technologies with deep learning models, which offer enhanced tools for suspect identification and criminal investigations. This chapter explores the entire landscape of data augmentation techniques in the context of face sketch synthesis. Beginning with a comprehensive overview of the domain, including the significance of face sketch synthesis in digital forensics, the chapter navigates through the pivotal role played by deep learning in refining these synthesis methodologies towards improvement. The purpose of data augmentation in enhancing the robustness of face sketch synthesis models forms a central theme, also setting the stage for a detailed exploration of its advanced techniques, domain-specific considerations, and evaluation metrics. Real-world case studies and comparative analyses have been presented to provide insights into the practical applications and effectiveness of data augmentation, while addressing the challenges and future directions in this rapidly evolving field. This chapter also explores the ethical dimensions and sustainable development implications associated with the development and application of these technologies.