Using Conditional GAN for Facial Recognition Enhancement

Increasing dependence of technology presents issues for security agencies in identifying suspects, stemming from the inadequacies of eyewitness-based sketching techniques. Conventional methods fail to effectively convert sketches into lifelike facial representations, frequently resulting in misidentifications. This study advocates for the application of Conditional Generative Adversarial Networks (CGANs) to improve the realism and precision of suspect images produced from sketches. Conditional Generative Adversarial Networks utilize the deep learning methodologies to generate the high-quality photos by acquiring the feature representations from a dataset of facial images and their accompanying sketches. The main aim of this study is to connect the forensic sketching with digital image reconstruction, allowing the law enforcement authorities to produce more accurate and recognizable facial images based on witness testimonies. This work seeks to enhance the criminal investigations by enhancing the precision of sketch-to-image translation, hence the accelerating suspect identification and the minimizing erroneous identifications. The proposed model incorporates the data augmentation, a CGAN-based synthesis mechanism, and an intuitive interface for real-time image production. This study enhances forensic applications of AI, illustrating the significant influence of deep learning on law enforcement and public safety.