Face Recognition System Based on Raspberry Pi Platform

Mustakim, Nafis; Hossain, Noushad; Rahman, Mohammad Mustafizur; Islam, Nadimul; Sayem, Zayed Hossain; Mamun, Md. Abdullah Al

doi:10.1109/icasert.2019.8934485

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…Once this is done, the facial landmarks are identified using the Dlib library. Each set of facial landmarks derived from the students' faces is saved in SQLite [31] and associated with an appropriate students' names. A Raspberry Pi camera built into a Raspberry Pi 3B was set up in the classroom in order to capture the images and transfer them to a cloud server through the RESTful application programming interface (API).…”

Section: Methodsmentioning

confidence: 99%

The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

Lin

Huang

2022

Electronics

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The aim of this study was to develop a real-time automatic attendance system (AAS) based on Internet of Things (IoT) technology and facial recognition. A Raspberry Pi camera built into a Raspberry Pi 3B is used to transfer facial images to a cloud server. Face detection and recognition libraries are implemented on this cloud server, which thus can handle all the processes involved with the automatic recording of student attendance. In addition, this study proposes the application of data serialization processing and adaptive tolerance vis-à-vis Euclidean distance. The facial features encountered are processed using data serialization before they are saved in the SQLite database; such serialized data can easily be written and then read back from the database. When examining the differences between the facial features already stored in the SQLite databases and any new facial features, the proposed adaptive tolerance system can improve the performance of the facial recognition method applying Euclidean distance. The results of this study show that the proposed AAS can recognize multiple faces and so record attendance automatically. The AAS proposed in this study can assist in the detection of students who attempt to skip classes without the knowledge of their teachers. The problem of students being unintentionally marked present, though absent, and the problem of proxies is also resolved.

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Section: Methodsmentioning

confidence: 99%

The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

Lin

Huang

2022

Electronics

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“…Face acknowledgment calculation by incorporating a couple of face acknowledgment strategies that combine LDA, PCA based Raspberry pi 3. This blend technique has given a precision of 97% [11] Acknowledgment continuously outlines carried out Raspberry Pi in this work, the framework had the option to deal with only four 4FPS [12]. Friendly, stand-alone entry control framework utilizing facial acknowledgment, decreased the face include size by 40% of the Gabor LBP facial attributes.…”

Section: Introductionmentioning

confidence: 95%

Analysis of facial emotion recognition rate for real-time application using NVIDIA Jetson Nano in deep learning models

Dudekula¹,

Nalluri²

2023

IJEECS

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Detecting <span>facial emotion expression is a classic research problem in image processing. Face expression detection can be used to help human users monitor their stress levels. Perceiving an individual's failure to communicate specific looks might help analyze early psychological disorders. several issues like lighting changes, rotations, occlusions, and accessories persist. These are not simply traditional image processing issues, yet additionally, action units that make gathering activity of facial acknowledgment troublesome look information, and order of the demeanor. In this study, we use Xception taking into account Xception and convolution neural network (CNN), which is easy to focus on incredible parts like the face, and visual geometric group (VGG-19) used to extract the facial feature using the OpenCV framework classifying the image into any of the basic facial emotions. NVIDIA Jetson Nano has a high video handling outline rate. Accomplishing preferable precision over the recently evolved models on software. The average accuracies for standard data set CK+,” on NVIDIA Jetson Nano, the accuracy rate is 97.1% in the Xception model in the convolutional neural network, 98.4% in VGG-19, and real-time environment accuracy using OpenCV, accuracy rate is 95.6%.</span>

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“…Sajjad M. et al [5] suggested that portable and compact devices like Raspberry Pi can be used to make face recognition systems that can enhance the law enforcement related services being an accurate and efficient device when trained properly. I. Gupta et al [6] implemented a model that eradicates the use of passwords & RFID cards in order to gain access to secured and permitted areas like office buildings, etc. by utilizing detection techniques like Haar.…”

Section: Literature Surveymentioning

confidence: 99%

Face Recognition using Raspberry Pi

Boxey¹,

Jadhav²,

Gade³

et al. 2022

JIPIRS

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Modern day security related concerns require modern solutions. With the advancement in technologies and proper knowledge, it has become easier to gain access to one’s id, password and passcodes by snooping, fishing, hacking, eavesdropping, and stealing which remains the same and forms base foundation to do malpractices. Hence, there is a constant threat to security, be it personal (if required), organizational, institutional, and so on. We need an approach that will address these security related concerns of systems that require passwords, passcodes, and RFID cards which can be tricked. This can be achieved with the help of technology called facial recognition.

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Face Recognition System Based on Raspberry Pi Platform

Cited by 14 publications

References 11 publications

The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

Analysis of facial emotion recognition rate for real-time application using NVIDIA Jetson Nano in deep learning models

Face Recognition using Raspberry Pi

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