Developed artificial neural network based human face recognition

Hussein, Maryam Mahmood; Mutlag, Ammar Hussein; Shareef, Hussain

doi:10.11591/ijeecs.v16.i3.pp1279-1285

Cited by 7 publications

(7 citation statements)

References 10 publications

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“…A neural network is a computational process that uses training to predict the output of a complex system [48]. Recently, ANNs have been used in various applications where they have achieved considerable success, resulting in their increasingly widespread use [49]. The ANN mimics biological neural activity.…”

Section: The Proposed Control System Based On Annmentioning

confidence: 99%

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

et al. 2022

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The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers' movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol's received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system's performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.INDEX TERMS ANN, COVID-19, control system, distance estimation, improved, PSO, RSSI Thus, distancing between people has become crucial for

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Section: The Proposed Control System Based On Annmentioning

confidence: 99%

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

et al. 2022

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“…This technique was used in [6] and the average accuracy of the proposed system was 96.84%. The authors in [7] proposed to improve the backpropagation artificial neural network (BP-ANN) for a better performance of the face recognition system. The proposed system yielded a success ratio of 82%.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition System Design and Implementation using Neural Networks

Alsyayadeh,

Aziz

et al. 2022

IJACSA

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Face recognition technology is used in biometric security systems to identify a person digitally before granting the access to the system or the data in it. There are many kidnappings or abduction cases happen around us, however, the kidnap suspects will be set free if there is lack of evidence or when the victims are not able to testify in court because they suffer from post-traumatic stress disorder (PTSD). The objectives of this study are, to develop a device that will capture the image of a kidnapper as evidence for future reference and send the captured image to the family of the victim through email, to design a face recognition system to be used in searching kidnap suspects and to determine the best training parameters for the convolution neural network (CNN) layers used by the proposed face recognition system. The accuracy of the proposed system is tested with three different datasets, namely the AT&T database, face database from [23] and a custom face dataset. The results are 87.50%, 92.19% and 95.93% respectively. The overall face recognition accuracy of the proposed system is 98.48%. The best training parameters for the proposed CNN model are kernel size of 5x5, 32 and 64 filters for first and second convolutional layers and learning rate of 0.001.

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“…To achieve this goal, six classifiers are being applied and their performances are compared to distinguish the most accurate one. Three fundamental stages of classification, face extraction, and feature extraction are suggested through Face Expression Recognition [11,12]. As a coral phase, feature selection is done to elecit the most important features that would have the most significance impact value [13] in which factors such as computational performance and the classification competence are manipulated by [14,15].…”

Section: Introductionmentioning

confidence: 99%

Classification techniques’ performance evaluation for facial expression recognition

Mahmood

Abdulrazaq

Zeebaree

et al. 2020

IJEECS

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<p><span>Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. </span></p>

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Developed artificial neural network based human face recognition

Cited by 7 publications

References 10 publications

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

Face Recognition System Design and Implementation using Neural Networks

Classification techniques’ performance evaluation for facial expression recognition

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