Forensic Recognition

Forensic recognition uses computational methods to analyze various forms of evidence, such as images, documents, and biological samples, aiding in criminal investigations and identification. Current research focuses on improving the accuracy and interpretability of these methods, employing techniques like deep learning architectures (e.g., convolutional neural networks, multiple-instance learning) and incorporating human-in-the-loop approaches to enhance model performance and trustworthiness. These advancements are crucial for improving the reliability and efficiency of forensic analysis, ultimately contributing to more accurate and just legal outcomes.