Automated Attendance Systems Using Face Recognition by K-Means Algorithms

Palanivel, N.; Aswinkumar, S; Balaji, Janani

doi:10.1109/icscan.2019.8878790

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…Palanivel N al., [19] developed a module that detects people's faces and generates tendency data. Face Recognition is most reliable when it comes to changes in brightness, posture, expression, and occlusion.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

Alhajim,

Salman FAHAMA,

T.A

2024

J. Al-Qadisiyah Comp. Sci. Math.

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The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

Alhajim,

Salman FAHAMA,

T.A

2024

J. Al-Qadisiyah Comp. Sci. Math.

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“…Krishnamurthy et al (2023) applied custom YOLO architectures to enhance object detection capabilities during endoscopic surgeries, illustrating the potential of YOLO in surgical settings [122]. Furthermore, Palanivel et al (2023) discussed the application of YOLOv8 in cancer diagnosis through medical imaging, further cementing YOLO's role in critical healthcare applications [123].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

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This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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“…For training and validation, the model uses Flickr's 140k dataset, which contains 50K actual faces and 50K deep fakes. Five convolutional blocks and one classifier block make up the architecture [16]. Pooling, activation, and dropout layers come after the thirteen convolution layers.…”

Section: Related Wordmentioning

confidence: 99%

“…Every extracted frame function as a snapshot, encapsulating a discrete point in the video sequence. This allows the system to examine the temporal development of facial characteristics and identify any anomalies that would indicate the use of deep fakes [29]. The algorithm makes sure that the analysis that follows concentrates on crucial facial areas, where profound false modifications are most likely to occur, by carefully extracting frames that feature localized faces.…”

Section: A Frame Extraction Of Videomentioning

confidence: 99%

Video Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm

2024

eatp

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A method called deepfake produces fake video and films with artificial or substituted faces. Deepfakes are turning into a worrying societal phenomenon because they may be used maliciously to spread harmful information, fabricate electronic convincing proof, make fake political news, and even participate in online harassment and fraud. Regarding the fight against the ubiquitous threat provided by deepfake videos, our suggested technique, named "Video Morphing Attack Detection Using CNN," is a stronghold. We provide our system the capacity to model temporal relationships across sequences and extract complex characteristics from video frames by integrating the ResNeXt-50 and LSTM neural network designs, respectively. Our method effectively detects abnormalities suggestive of video morphing assaults via forward propagation and SoftMax activation. We guarantee strong detection performance while maintaining the veracity and integrity of visual information by utilizing dynamic face localization and specific assessment measures. Our system provides a comprehensive solution to reduce the negative impacts of deep fake manipulation in visual media, including vital functionalities for picture conversion, prediction creation, and data pretreatment. Convolutional neural network architecture ResNeXt-50 is a member of the ResNeXt family, which was first presented as an advancement of the ResNet design. To overcome certain constraints of conventional convolutional neural networks (CNNs), ResNeXt aims to achieve state-of-the-art performance in image classification tasks while managing intricate visual input. The "50" in ResNeXt-50 stands for the network's depth or the total number of layers it has. ResNeXt-50 comprises 50 layers in total, comprising fully connected, pooling, and convolutional layers.

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Automated Attendance Systems Using Face Recognition by K-Means Algorithms

Cited by 12 publications

References 13 publications

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Video Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm

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