Face Recognition Using Eigenface Algorithm on Laptop Camera

Rosnelly, Rika; Simanjuntak, Mutiara; Sitepu, Ade Clinton; Azhari, Mulkan; Kosasi, Sandy; Husen,

doi:10.1109/citsm50537.2020.9268907

Cited by 11 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We aimed to achieve over 90% accuracy in face recognition, and our results show a commendable performance of 95.64% accuracy, exceeding our initial target. We also explored other methods such as Convolutional Neural Network (CNN) based face recognition [25], which demands extensive data and computational resources; LBPH method [26], which produces long histograms leading to slower recognition speeds; and the EigenFace [27] algorithm, which is sensitive to lighting variations. This study [28] uses the "dlib" library, employing Histogram Oriented Gradients (HOG) for feature extraction and Support Vector Machines (SVM) for differentiating face and non-face regions.…”

Section: Face Recognitionmentioning

confidence: 99%

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

Sanjar,

Bang,

Ryue

et al. 2024

CMC

View full text Add to dashboard Cite

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe, navigable routes. Traditional approaches primarily focus on broad applications such as wayfinding, obstacle detection, and fall prevention. However, there is a notable discrepancy in applying these technologies to more specific scenarios, like identifying distinct food crop types or recognizing faces. This study proposes a real-time application designed for visually impaired individuals, aiming to bridge this research-application gap. It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27% and 95.64%, respectively. These results represent a significant contribution to the field of assistive technologies, providing visually impaired users with detailed and relevant information about their surroundings, thereby enhancing their mobility and ensuring their safety. Additionally, it addresses the vital aspects of social engagements, acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals, and highlights recent developments in prototype systems aimed at assisting with face recognition tasks. This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

show abstract

Section: Face Recognitionmentioning

confidence: 99%

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

Sanjar,

Bang,

Ryue

et al. 2024

CMC

View full text Add to dashboard Cite

show abstract

“…Rosnelly et al [16] presented a study on the use of the Eigenface algorithm for facial recognition using a laptop camera. The authors of the paper claimed that their proposed system improved the accuracy of face recognition compared to traditional methods when using a laptop camera, which typically had lower resolution and worse lighting conditions than other types of cameras.…”

Section: Svmmentioning

confidence: 99%

Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN

Lee,

Ng,

Yoong

2023

JIWE

View full text Add to dashboard Cite

Many blind individuals have difficulties in recognizing people’s facial expression which may impact their social interaction. With the recognition, the blind individuals can accurately interpret and respond to the emotions. There is a lack in the existing application with the combination of face and facial expressions recognition. The blind individuals have to rely on multiple applications to accomplish the same task, making it difficult and time-consuming for them to use. The paper aims to recognize faces and facial expressions for blind individuals and provides feedback in real-time. Three face detection algorithms of Haar Cascade Classifier, Dlib, and RetinaFace are compared. Dlib is chosen to process with Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). It loads the pre-trained model, computes the HOG features, slide the window scanning at different scales, classify the windows using the SVM classifier, generate bounding boxes, and applying non-maximum suppression. ResNet50 architecture is employed to recognize face and Convolutional Neural Networks (CNN) is applied to recognize facial expression. The training accuracy is 70% and validation accuracy is 60%.

show abstract

“…The Eigenface technique is used in [4] for building face recognition software and the average accuracy for their face recognition software is 85%. Next, a hybrid approach which consists of the Haar Cascades and Eigenface methods that can detect multiple faces in a single detection process is proposed in [5].…”

Section: Related Workmentioning

confidence: 99%

Face Recognition System Design and Implementation using Neural Networks

Alsyayadeh,

Aziz

et al. 2022

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Face Recognition Using Eigenface Algorithm on Laptop Camera

Cited by 11 publications

References 8 publications

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN

Face Recognition System Design and Implementation using Neural Networks

Contact Info

Product

Resources

About