Deep Learning-Based Text-to-Image synthesis for Criminal Face Generation Deep Learning-Based Text-to-Image Synthesis for Criminal Face Generation

In contemporary law enforcement and criminal investigations, the need for accurate suspect identification remains paramount. This study introduces a novel approach to generate criminal facial images utilizing Deep Convolutional Generative Adversarial Networks (DCGAN) trained on the Celeb dataset. The Celeb dataset comprises a vast collection of celebrity images, serving as a rich source for training generative models due to its diverse facial features.

The proposed method harnesses the power of DCGAN architecture, renowned for its ability to learn complex data distributions and generate high-quality synthetic images. By fine-tuning the DCGAN model on the Celeb dataset, we enable it to synthesize realistic criminal facial images that encapsulate a variety of characteristics commonly associated with suspects in criminal investigations.

Key aspects of our methodology include preprocessing techniques to extract facial features from the Celeb dataset, training the DCGAN model to learn the underlying patterns and distributions of criminal facial attributes, and evaluating the generated images for realism and applicability in forensic contexts.

The results demonstrate the effectiveness of the proposed approach in generating diverse and realistic criminal facial images, showcasing its potential utility in aiding law enforcement agencies and forensic experts in suspect identification and criminal investigations. Furthermore, the scalability and adaptability of the DCGAN framework suggest promising avenues for future research and development in the field of forensic facial synthesis.

KEYWORDS: Deep Learning, Text-to-Image Generation, Criminal Faces, DCGAN, Celeb Dataset, Facial Synthesis, Image Generation