Forensic Face Sketching and Recognition using Deep Learning

The research proposes a new forensic face sketching and recognition system built on deep learning techniques to improve criminal identification capabilities. The method uses a mix of Convolutional Neural Networks (CNNs) and also Generative Adversarial Networks (GANs) to produce accurate facial drawings from text descriptions that can then be compared to real-world face photos. This method resolves traditional sketch recognition issues through automation which results in improved precision and more consistent outcome generation. A real and synthesized facial image dataset provides foundation for training as the system implements both augmentation and normalization methods. thrown results show that sketch-to-photograph matching provides accurate outcomes which support reliable forensic investigation output. The new system drives substantial improvements to forensic technology by executing more precise identifications together with reduced human mistakes.