The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

Alsalibi, Bisan; Venkat, Ibrahim; Subramanian, K. G.; Lutfi, Syaheerah Lebai; Wilde, Philippe De

doi:10.1145/2791121

Cited by 18 publications

(8 citation statements)

References 143 publications

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“…The first step for considering images in membrane computing is the use of two-dimensional objects, called arrays. 3 Array grammars have been widely studied in the literature. They can be considered as a straightforward extension of string grammars to two-dimensional pictures.…”

Section: First Stepsmentioning

confidence: 99%

“…2 Some of these applications were collected in the volume [34]. 3 An overview of 2D picture array generating models based on membrane computing can be found in [164]. 4 Adapted from the Example 1 in [22].…”

Section: First Stepsmentioning

confidence: 99%

“…Detection of more complex structures, as corners in the images, has been explored in [8]. Alsalibi et al study different bio-inspired approaches for the problem of face recognition in [3] and consider bridging membrane computing and evolutionary computing [182]. The problem of face recognition is also considered in [2] where a bat algorithm [177] under the framework of membrane computing (MC) is employed.…”

Section: Final Conclusionmentioning

confidence: 99%

See 2 more Smart Citations

Membrane computing and image processing: a short survey

2019

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Membrane computing is a well-known research area in computer science inspired by the organization and behavior of live cells and tissues. Their computational devices, called P systems, work in parallel and distributed mode and the information is encoded by multisets in a localized manner. All these features make P systems appropriate for dealing with digital images. In this paper, some of the open research lines in the area are presented, focusing on segmentation problems, skeletonization and algebraic-topological aspects of the images. An extensive bibliography about the application of membrane computing to the study of digital images is also provided.

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Section: First Stepsmentioning

confidence: 99%

Section: First Stepsmentioning

confidence: 99%

Section: Final Conclusionmentioning

confidence: 99%

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Membrane computing and image processing: a short survey

2019

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“…Due to their superior performance, optimization algorithms have been used to solve many real-world problems in various fields. Optimization algorithms can be employed in different applications, such as computer networks [ 5 ], intrusion detection [ 6 ], data mining [ 7 ], face recognition [ 8 ], and clustering [ 9 ]. Furthermore, these algorithms may be utilized in other scientific contexts like electrical engineering (i.e., power systems [ 10 ] and telecommunications [ 11 ]) and other industries (i.e., robotics [ 12 ] and transportation [ 13 ]).…”

Section: Introductionmentioning

confidence: 99%

Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

Salehan

Deldari

2021

J Supercomput

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This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.

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“…MC is a new active branch of natural computing that simulates the function and structure of living cells and tissues, abstracting their biochemical reactions and material exchanges [19]. One of the most prominent features of MC is its capability of generating exponential growth space over a polynomial time, which makes it a promising method to resolve the conflict between the ever-increasing amount of data in the image processing field and the backward computing power of conventional computer [20]. In recent years, image edge detection and image segmentation [21,22,23,24], image smoothing [25], obtaining homology groups of 2D images [26,27], counting cells [28], Enzymatic numerical P systems image thinning [29] and corner detection [30] in MC framework have been vividly studied.…”

Section: Introductionmentioning

confidence: 99%

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

et al. 2019

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Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

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The Impact of Bio-Inspired Approaches Toward the Advancement of Face Recognition

Cited by 18 publications

References 143 publications

Membrane computing and image processing: a short survey

Membrane computing and image processing: a short survey

Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

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