A Human-Centered Approach for Tattoo Detection Using Convolutional Neural Networks: A Case Study in Forensic Applications

This paper presents the design, development, and evaluation of a web-based tattoo detection system that integrates Convolutional Neural Networks (CNNs) with a Human-Centered Design (HCD) approach for forensic applications. Manual identification of tattoos in forensic investigations is often slow, error-prone, and subject to human bias, highlighting the need for automated solutions. To address this, we develop a system that combines deep learning with usability-driven design. The methodology involved expert and public surveys, iterative wireframe refinements, and model training using TensorFlow with a fine-tuned ResNet-50 network. Forensic professionals emphasized the importance of accuracy, privacy, and advanced search filters, while general users prioritized usability and transparency. Preliminary evaluations suggest that the system enhances forensic workflows by providing an intuitive interface and automated tattoo identification capabilities. Ethical considerations, such as fairness and bias mitigation, were also integrated into the design. These findings highlight the potential of AI-powered tattoo detection in forensic science, which offers both technical advancements and practical usability improvements.