An enhanced Biometric-based Face Recognition System using Genetic and CRO Algorithms

Surakhi, Ola; Khanafseh, Mohammad; Jaffal, Yasser

doi:10.25046/aj030316

Cited by 2 publications

(2 citation statements)

References 11 publications

(10 reference statements)

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“…They also suggested a method called the Multimodal Deep Face Recognition (MDFR) system, which adds feature representations by training a deep belief network (DBN) in place of pixel intensity representations on top of the local feature representations. Surakhi et al [42] proposed a meta-heuristic algorithm based face recognition system. Genetic algorithm and Chemical Reaction Optimization (CRO) Algorithm is used to search for the best point in the image which will be used as a pivot and their performances are compared.…”

Section: Local Feature Approachmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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Section: Local Feature Approachmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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“…Heuristic genetic algorithm is tried to enhance every population by select best individual. For the above reason the important of local methods appear to obtain the best outcomes [21]. Taking into account without local techniques no method can be found for optimum solutions, and there is no guarantee to cross the local optimal, and its very time consuming without any change, and it remain in stuck as well.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

An enhanced hybrid genetic algorithm for solving traveling salesman problem

Ali¹,

Rasheed²,

Ali³

2020

IJEECS

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<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

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An enhanced Biometric-based Face Recognition System using Genetic and CRO Algorithms

Cited by 2 publications

References 11 publications

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

An enhanced hybrid genetic algorithm for solving traveling salesman problem

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