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Forensic face sketching has long been a cornerstone in criminal investigations, offering a visual representation of suspects based on eyewitness descriptions. This paper provides a comprehensive overview of the historical evolution and intricacies involved in the creation of these sketches, underscoring the challenges posed by time constraints and the necessity for skilled artists proficient in translating verbal descriptions into accurate depictions. In response to these challenges, this research proposes a forward-looking solution that embraces technological advancements. The envisioned solution revolves around the development of a dedicated standalone application tailored for composite sketching. This application will revolutionize the process by introducing intuitive drag-and-drop functionalities, effectively mitigating the reliance on specialized forensic artists. The application autonomously generates composite sketches based on input descriptions, thereby streamlining the investigative process and significantly reducing the time required for suspect identification. Moreover, the application will boast seamless integration with police databases, facilitating automated matching and comparison against a vast repository of existing records. This integration not only enhances efficiency but also opens up avenues for further research, paving the way for continuous improvement and refinement of the sketching and recognition capabilities. In summary, this paper advocates for a paradigm shift in the realm of forensic face sketching through the implementation of an innovative technological solution. By embracing automation and leveraging the capabilities of deep learning, this proposed application aims to revolutionize suspect identification processes, ultimately enhancing the effectiveness of criminal investigations while also serving as a catalyst for ongoing advancements in the field.
Chorage et al. (Sat,) studied this question.