9th International Conference on ICT for Sustainable Development

Authors - Aditya Naidu Kolluru, G. Ram Sundar, Sindhu C, P. Arun Prakash, Kaila Jagadish Reddy
Abstract - The common medium of computer vision and natural language processing, text-prompted face detection with Generative Adversarial Networks (GANs) is a developing field. The architecture, techniques, and applications of GAN-based models for producing lifelike face images under the guidance of textual cues are thoroughly examined in this paper. Based on the semantic information contained in textual descriptions, our suggested framework uses a conditional GAN architecture to generate facial features that match given text prompts. The model acquires the ability to produce face images that correspond with specific traits or features mentioned in the text through adversarial training. We examined generator and discriminator networks, joint training strategies, text encoding, and fine-tuning techniques customized for text-prompted face detection tasks. The effectiveness and promise of the suggested strategy are shown by empirical analysis and case studies in a range of fields, from creative expression and entertainment to security and surveillance. We also explore future research directions, opportunities, and challenges in enhancing text-prompted face detection with GANs. To bridge the gap between textual cues and visual content, this research advances the field of artificial intelligence and computer vision research, opening the door to new applications and advancements.