Mahmoud Hassaballah

TL;DR: The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm

TL;DR: This chapter introduces basic notation and mathematical concepts for detecting and describing image features, and discusses properties of perfect features and gives an overview of various existing detection and description methods.

TL;DR: Experimental results proved that the proposed HHO-SVM approach achieved the highest capability to obtain the optimal features compared with several well-established metaheuristic algorithms including: Particle Swarm Optimization (PSO), Simulated Annealing (SA), Dragonfly Algorithm (DA), Butterfly Optimization Al algorithm (BOA), Moth-Flame OptimizationAlgorithm (MFO), Grey Wolf Optimizer (GWO), Sine Cosine Algorithm

TL;DR: The goal of this study is to discuss the significant challenges involved in the adaptation of existing face recognition algorithms to build successful systems that can be employed in the real world and propose several possible future directions for face recognition.

TL;DR: Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unclear or difficult to solve.