Open Access

ARTICLE

Novel Framework for Generating Criminals Images Based on Textual Data Using Identity GANs

by Mohamed Fathallah^1,*, Mohamed Sakr², Sherif Eletriby²

1 Department of Computer Science, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt
2 Department of Computer Science, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt

* Corresponding Author: Mohamed Fathallah. Email:

Computers, Materials & Continua 2023, 76(1), 383-396. https://doi.org/10.32604/cmc.2023.039824

Received 19 February 2023; Accepted 14 April 2023; Issue published 08 June 2023

Abstract

Text-to-image generation is a vital task in different fields, such as combating crime and terrorism and quickly arresting lawbreakers. For several years, due to a lack of deep learning and machine learning resources, police officials required artists to draw the face of a criminal. Traditional methods of identifying criminals are inefficient and time-consuming. This paper presented a new proposed hybrid model for converting the text into the nearest images, then ranking the produced images according to the available data. The framework contains two main steps: generation of the image using an Identity Generative Adversarial Network (IGAN) and ranking of the images according to the available data using multi-criteria decision-making based on neutrosophic theory. The IGAN has the same architecture as the classical Generative Adversarial Networks (GANs), but with different modifications, such as adding a non-linear identity block, smoothing the standard GAN loss function by using a modified loss function and label smoothing, and using mini-batch training. The model achieves efficient results in Inception Distance (FID) and inception score (IS) when compared with other architectures of GANs for generating images from text. The IGAN achieves 42.16 as FID and 14.96 as IS. When it comes to ranking the generated images using Neutrosophic, the framework also performs well in the case of missing information and missing data.

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Keywords

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