Facial Recognition using Enhanced Facial Features k-Nearest Neighbor (k-NN) for Attendance System

Tee, Tian Xiang; Khoo, Hee Kooi

doi:10.1145/3417473.3417475

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…(E.g., [13], [14]). To categorize mood from extracted feature values, a variety of approaches employed support vector machines (SVM), K-neighbors neighbors (KNN), and random forest [15]- [17].…”

Section: Facial Expression Recognition 211 Machine Learning Based App...mentioning

confidence: 99%

Facial expression recognition of masked faces using deep learning

Hdioud

Tirari

2023

IJ-AI

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Facial expression recognition (FER) represents one of the most prevalent forms of interpersonal communication, which contains rich emotional information. But it became even more challenging during the times of COVID, where face masks became a mandatory protection measure, leading to the challenge of occluded lower-face during facial expression recognition. In this study, deep convolutional neural network (DCNN) represents the core of both our full-face FER system and our masked face FER model. The focus was on incorporating knowledge distillation in transfer learning between a teacher model, which is the full-face FER DCNN, and the student model, which is the masked face FER DCNN via the combination of both the loss from the teacher soft-labels vs the student soft labels and the loss from the dataset hard-labels vs the student hard-labels. The teacher-student architecture used FER2013 and a masked customized version of FER2013 as datasets to generate an accuracy of 69% and 61% respectively. Therefore, the study proves that the process of knowledge distillation may be used as a way for transfer learning and enhancing accuracy as a regular DCNN model (student only) would result in 46% accuracy compared to our approach (61% accuracy).

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Section: Facial Expression Recognition 211 Machine Learning Based App...mentioning

confidence: 99%

Facial expression recognition of masked faces using deep learning

Hdioud

Tirari

2023

IJ-AI

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“…Основная задача классификатора -получить относительные расстояния между чертами лица. К широко используемым в классификаторах алгоритмам можно отнести: метод опорных векторов, метод kближайших соседей, случайный лес [35][36][37]. Отдельно стоит отметить применение нейронных сетей различной архитектуры: свёрточные сети, множественные свёрточные сети, глубокие нейронные сети [38][39][40][41][42][43][44][45].…”

Section: The Main Task Of the Research Is To Analyze The Vulnerabilit...unclassified

Comparative Analysis of Vulnerabilities in Biometric Face Recognition Systems

Dorofeev¹

2022

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В статье рассматриваются современные подходы к решению задачи распознавания лиц, построению биометрических систем идентификации по лицам, а также основные проблемы и уязвимости подобных систем. Особое внимание уделено вопросам безопасности, а также проблемам, связанным с атаками на такие системы: использование подделок, распечатанных фотографий и масок, визуализированных и сгенерированных двумерных и трёхмерных изображений и видео лиц. Рассмотрены современные подходы к решению таких проблем, к противодействию подобным атакам. Также рассмотрены правовые аспекты, приведены законы, регулирующие обработку биометрических данных граждан РФ, и оценены риски атак на биометрические системы.Основная задача исследования -провести анализ уязвимостей биометрических систем распознавания лиц и указать основные способы устранения этих уязвимостей.

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“…In the second component, a classifier is used to obtain the distance of the face features to differentiate among several individuals. Some of the most widely used algorithms in facial recognition tasks are Support Vector Machine (SVM), K-Neighbors Neighbors (KNN), and Random Forest (RF), for their effectiveness in multiclass classification [ 4 , 41 , 42 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

Efficient Face Recognition System for Operating in Unconstrained Environments

et al. 2021

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Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

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Facial Recognition using Enhanced Facial Features k-Nearest Neighbor (k-NN) for Attendance System

Cited by 6 publications

References 4 publications

Facial expression recognition of masked faces using deep learning

Facial expression recognition of masked faces using deep learning

Comparative Analysis of Vulnerabilities in Biometric Face Recognition Systems

Efficient Face Recognition System for Operating in Unconstrained Environments

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