Generation of Facial Drawings Using Generative Adversarial Networks
Debabrata Datta1, Abhradeep Dey2, Adityam Ghosh3, Rishabh Tiwari4
1Debabrata Datta, Department of Computer Science, St. Xavier’s College(Autonomous), Kolkata, India.
2Abhradeep Dey, Department of Computer Science, St. Xavier’s College(Autonomous), Kolkata, India.
3Adityam Ghosh, Department of Computer Science, St. Xavier’s College(Autonomous), Kolkata, India.
4Rishabh Tiwari, Department of Computer Science, St. Xavier’s College(Autonomous), Kolkata, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 746-750 | Volume-8 Issue-6, August 2019. | Retrieval Number: F7988088619/2019©BEIESP | DOI: 10.35940/ijeat.F7988.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Facial sketches are widely used in judicial and legal proceedings. Law enforcers use facial sketches to help them with the visual aspects of the case, using witness descriptions and video footage. However, drawing forensic sketches by hand is a time-consuming procedure and a situation may arise where the authorities have less time in hand to solve a case. The present research work aims to create a basic model which can generate facial images from a given set of input features; similar to what a forensic artist does, thus, enabling a faster and efficient sketching procedure. In this work, a category of generative algorithms, called Generative Adversarial Networks has been used to build this model. To train this model, a dataset of anime girls has been used and thus it can only generate the same, making sure that the generated image contains the input features.
Keywords: Artificial Intelligence, Auxiliary Classifier Generative Adversarial Network, Deep Learning, Face Generation, Forensic Art, Generative Adversarial Network, Machine Learning, Neural Networks.