Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study

Alsrehin, Nawaf O.; Al-Taamneh, Mutasem A.

doi:10.1109/icict50521.2020.00032

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…One could consider biometrics technology such as face recognition for driver's authentication. Jiang [21] and Alsrehin & Al-Taamneh [22] compared the traditional and intelligent face recognition methods suitable for such applications. Depending on the complexity of the face recognition application, Anagha & Ram [23] shared possible solutions to address some of the shortcomings.…”

Section: Related Workmentioning

confidence: 99%

"IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management"

Rudrusamy,

Teoh,

Pang

et al. 2023

IJIE

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Curbing road accidents havealways been one of the utmost priority of nations worldwide. In Malaysia, the Traffic Investigation and Enforcement Department reported that Malaysia’s total number of road accidents haveincreased from 373,071 to 533,875 in the last decade. One of the significant causes of road accidentsis the driver’s behaviors. However, to regulate drivers’ behavior by the enforcement team or fleet operatorsischallenging, especially for heavy vehicles. In our research, we have proposed the Internet of Things (IoT) scalability framework and its’ emerging technologies to monitor and alert driver’s behavioral and driving patterns to reduceroad accidents. To prove this work, we have implementeda lane tracking,and iris detection algorithm, to monitor and alert the driver’s behavior when the vehicle sways away from the lane, and to detect if the driver is feeling drowsy. We implemented electronic devices such as cameras, a global positioning system module, a global system communication module, and a microcontroller as the hardware for an intelligent system in the vehicle. We also appliedface recognition for person identification using the same in-vehicle camera and recorded the working duration for authentication and operation health monitoring. With the GPS module, we monitored and alerted against permissible vehicle’s speed accordingly. We integrated IoT on the system for the fleet centre to monitor and alert the driver’s behavioral activities in real-time through the user access portal. We have validated it successfully on Malaysian roads. The outcome of this pilot project ensuresthe safety of drivers, public road users, and passengers. The impact of this framework leads to a new regulation by the government agencies towards merit and demerit system, real-time fleet monitoring of intelligent transportation systems, and socio-economy such as cheaper health premiums. The big data can be used to predict the driver’s behavioral in the future.

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Section: Related Workmentioning

confidence: 99%

"IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management"

Rudrusamy,

Teoh,

Pang

et al. 2023

IJIE

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“…Several mathematical models capable of processing under adverse conditions have been proposed to address these challenges. Constrained frontal faces shall be analysed by facial expression classifers [6]. Subspace analysis techniques [7] require extensive training and are not suitable.…”

Section: Classes Of Emotionmentioning

confidence: 99%

Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

Smitha

Sendhilkumar

Mahalakshmi

2023

Scientific Programming

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Human emotion recognition from videos involves accurately interpreting facial features, including face alignment, occlusion, and shape illumination problems. Dynamic emotion recognition is more important. The situation becomes more challenging with multiple persons and the speedy movement of faces. In this work, the ensemble max rule method is proposed. For obtaining the results of the ensemble method, three primary forms, such as CNNHOG-KLT, CNNHaar-SVM, and CNNPATCH are developed parallel to each other to detect the human emotions from the extracted vital frames from videos. The first method uses HoG and KLT algorithms for face detection and tracking. The second method uses Haar cascade and SVM to detect the face. Template matching is used for face detection in the third method. Convolution neural network (CNN) is used for emotion classification in CNNHOG-KLT and CNNHaar-SVM. To handle occluded images, a patch-based CNN is introduced for emotion recognition in CNNPATCH. Finally, all three methods are ensembles based on the Max rule. The CNNENSEMBLE for emotion classification results in 92.07% recognition accuracy by considering both occluded and nonoccluded facial videos.

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“…Furthermore, the most significant feature that neural network possesses is self-adaptability, which means it is able to enhance itself through iteration. The most representative neural network methods in face recognition are multi-level BP networks and RBF networks [28,29].…”

Section: Plos Onementioning

confidence: 99%

A face recognition software framework based on principal component analysis

et al. 2021

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Face recognition, as one of the major biometrics identification methods, has been applied in different fields involving economics, military, e-commerce, and security. Its touchless identification process and non-compulsory rule to users are irreplaceable by other approaches, such as iris recognition or fingerprint recognition. Among all face recognition techniques, principal component analysis (PCA), proposed in the earliest stage, still attracts researchers because of its property of reducing data dimensionality without losing important information. Nevertheless, establishing a PCA-based face recognition system is still time-consuming, since there are different problems that need to be considered in practical applications, such as illumination, facial expression, or shooting angle. Furthermore, it still costs a lot of effort for software developers to integrate toolkit implementations in applications. This paper provides a software framework for PCA-based face recognition aimed at assisting software developers to customize their applications efficiently. The framework describes the complete process of PCA-based face recognition, and in each step, multiple variations are offered for different requirements. Some of the variations in the same step can work collaboratively and some steps can be omitted in specific situations; thus, the total number of variations exceeds 150. The implementation of all approaches presented in the framework is provided.

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Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study

Cited by 6 publications

References 25 publications

"IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management"

"IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management"

Ensemble Convolution Neural Network for Robust Video Emotion Recognition Using Deep Semantics

A face recognition software framework based on principal component analysis

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