Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Pei, Zhao; Xu, Hang; Zhang, Yanning; Guo, Min; Yang, Yee‐Hong

doi:10.3390/electronics8101088

Cited by 40 publications

(18 citation statements)

References 38 publications

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“…Convolutional neural network (CNN) becomes a popular choice for the face recognition attendance system approach. Recently [1] experimented class attendance system using data augmentation to overcome the insufficient sample issue on CNN. The experiment fine-tuned the VGG-16 network to accomplished face recognition with an accuracy of 86.3%.…”

Section: Related Workmentioning

confidence: 99%

“…Recently many attendance systems proposed using the face recognition advantage. It has reduced the burden of taking attendance manually, prevent fraud and embedded in the updated technology [1]. Among biometrics techniques, face recognition provides a contactless method that complies with current scenarios requirement compared to fingerprint analysis, palm prints and iris recognition.…”

Section: Introductionmentioning

confidence: 99%

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Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Suhaimin¹,

Hijazi

Kheau

et al. 2021

Bulletin EEI

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Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Suhaimin¹,

Hijazi

Kheau

et al. 2021

Bulletin EEI

View full text Add to dashboard Cite

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“…Many face recognition algorithms through deep learning have achieved promising results with large numbers of samples. The authors of [ 13 ] solved this problem using data augmentation through geometric transformation, changing the brightness of the image, and applying different filter operations. By fine-tuning the VGG model, their accuracy achieved 86.3%, outperforming PCA and LBPH.…”

Section: Related Workmentioning

confidence: 99%

Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks

Santos

Rocha

Reis

et al. 2020

Sensors

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Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.

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“…Pei et al made a student attendance system using face recognition based on deep learning [30]. Researchers revealed the problem faced was the difficulty in getting a large amount of training data.…”

Section: Peng Et Al Introduced a Cnn Called Nirfacenetmentioning

confidence: 99%

A Survey of Face Recognition based on Convolutional Neural Network

Saragih

2022

Indonesian J. of Inf. Syst.

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Face recognition is one of the interesting research topics in the field of computer vision. In recent years, deep learning methods, especially the Convolutional Neural Network, have progressed. One of the successes of CNN is in face recognition. Face recognition by computer is a technique done so that the computer can automatically recognize faces in an image. Various researchers have conducted related research on facial recognition. This survey presents researches related to face recognition based on Convolutional Neural Network that has been conducted. The studies used are studies that have been published in the last five years. It was performed to determine the renewal that emerged in face recognition based on Convolutional Neural Network. The basic theory of the Convolutional Neural Network, face recognition, and description of the database used in various researches are also discussed. Hopefully, this survey can provide additional knowledge regarding face recognition based on the Convolutional Neural Network.

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Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Cited by 40 publications

References 38 publications

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks

A Survey of Face Recognition based on Convolutional Neural Network

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