Open Access

ARTICLE

Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

Summra Saleem^1,2, M. Usman Ghani Khan^1,2, Tanzila Saba³, Ibrahim Abunadi³, Amjad Rehman^3,*, Saeed Ali Bahaj⁴

1 Department of Computer Science, UET, Lahore, Pakistan
2 Al-Khwarizmi Institute of Computer Science, UET, Lahore, Pakistan
3 Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University, Riyadh, 11586, Saudi Arabia
4 MIS Department College of Business Administration, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia

* Corresponding Author: Amjad Rehman. Email:

Computers, Materials & Continua 2022, 70(1), 505-521. https://doi.org/10.32604/cmc.2022.018871

Received 24 March 2021; Accepted 15 May 2021; Issue published 07 September 2021

Abstract

Image translation plays a significant role in realistic image synthesis, entertainment tasks such as editing and colorization, and security including personal identification. In Edge GAN, the major contribution is attribute guided vector that enables high visual quality content generation. This research study proposes automatic face image realism from freehand sketches based on Edge GAN. We propose a density variant image synthesis model, allowing the input sketch to encompass face features with minute details. The density level is projected into non-latent space, having a linear controlled function parameter. This assists the user to appropriately devise the variant densities of facial sketches and image synthesis. Composite data set of Large Scale CelebFaces Attributes (ClebA), Labelled Faces in the Wild (LFWH), Chinese University of Hong Kong (CHUK), and self-generated Asian images are used to evaluate the proposed approach. The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.

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Citations