[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed, Sulayman; Frikha, Mounir; Hussein, Taha Darwassh Hanawy; Rahebi, Javad

doi:10.1155/2021/6621540

Cited by 37 publications

(20 citation statements)

References 66 publications

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“…The most famous indices include similarity index (SI), accuracy, sensitivity, and specificity, for every one of which it is necessary to calculate initial indices such as true positive (TP), true negative (TN), false positive (FP), and false negative (FN). Then SI, accuracy, sensitivity, and specificity can be obtained from the following equations [ 24 – 29 ]:

…”

Section: Implementation and Analysismentioning

confidence: 99%

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Iswisi

Karan

Rahebi

2021

BioMed Research International

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The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k -NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

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…”

Section: Implementation and Analysismentioning

confidence: 99%

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Iswisi

Karan

Rahebi

2021

BioMed Research International

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“…(2) The face detection algorithm based on optical flow technique is utilised to extract face features while developing the intelligent face recognition system, and the design of the intelligent face recognition system is finished. Face detection, face identification, and face training are the system's core functional elements [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Face Recognition System Based on Universal Design Concept

Shin

2022

Computational Intelligence and Neuroscience

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The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people’s life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users’ physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.

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“…The process of facial recognition includes image acquisition, face detection, feature extraction, face recognition, and finally, verification or identification. The techniques of feature extraction in face recognition such as Principal Component Analysis (PCA) [6], Linear Discriminant Analysis (LDA), Local Binary Patterns (LBP) [7], Elastic Bunch Graph Matching (EBGM) [8], Gabor Wavelet [9], and Convolutional Neural Networks (CNN) [10].…”

Section: Introductionmentioning

confidence: 99%

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

2022

KSII TIIS

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Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 -45 degrees) and left (30 -45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

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[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Cited by 37 publications

References 66 publications

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Intelligent Face Recognition System Based on Universal Design Concept

Web-based University Classroom Attendance System Based on Deep Learning Face Recognition

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