Forensic face-sketch creation and recognition using AWS Rekognition and facenet
Abstract
Forensic science faces significant challenges due to the time-consuming nature of hand-drawn face sketches, which hinder prompt criminal identification. Traditional methods, relying on forensic artists, are not only resource-intensive but also plagued by delays. The integration of modern technologies, such as deep learning and cloud infrastructure, presents a complex challenge, particularly concerning the interpretation of diverse user inputs and ensuring real-time compatibility with police databases while addressing data privacy concerns.
To address these challenges effectively, this paper proposes a comprehensive solution. The focal point is on robust algorithmic development, a user-friendly design, and a secure cloud infrastructure. The envisioned standalone application aims to revolutionize the face sketch creation process, enabling users to effortlessly generate composite sketches through a drag-and-drop interface. By leveraging advanced deep learning techniques, the application seeks to bridge the gap between intuitive sketch creation and efficient facial recognition, thereby streamlining the entire investigative process.
Sketch artists are utilized in criminal investigations to create facial composites based on eyewitness descriptions. However, this method is time-consuming, subjective, and reliant on the availability of skilled artists, posing challenges in accuracy and resource allocation for law enforcement agencies. As technology advances, there is a growing need to explore more efficient and objective alternatives to traditional hand-drawn sketches.
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