Feature Extraction Efficient for Face Verification Based on Residual Network Architecture

Khamket, Thananchai; Surinta, Olarik

doi:10.1007/978-3-030-80253-0_7

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…This is an accurate and effective face detection algorithm. The second one is the Max-Margin Object Detection (MMOD) CNN Face detector [27]. This is a very accurate and very robust, from different viewpoints Face detection in lighting and occlusion conditions.…”

Section: Face-recognition Library In Pythonmentioning

confidence: 99%

Evaluating Secure Methodology for Photo Sharing in Online Social Networks

Alwabel

Alsuhibany

2022

Applied Sciences

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Social media has now become a part of people’s lives. That is, today people interact on social media in a way that never happened before, and its important feature is to share photos and events with friends and family. However, there are risks associated with posting pictures on social media by unauthorized users. One of these risks is the privacy violation, where the published pictures can reveal more details and personal information. Since this issue has not yet investigated, this paper thus evaluates a methodology to address this issue, which is a precedent of its kind. In particular, our methodology relies on effective systems for detecting faces and recognizing faces in published images using facial recognition techniques. To evaluate the proposed idea, we developed an application using convolutional neural network (CNN) and the results showed that the proposed methodology can protect privacy and reduce its violation on online social networks.

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Section: Face-recognition Library In Pythonmentioning

confidence: 99%

Evaluating Secure Methodology for Photo Sharing in Online Social Networks

Alwabel

Alsuhibany

2022

Applied Sciences

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“…Each student, on enrolment, must provide a headshot which presents a complete set of facial features, as shown in Figure 3a. The convolutional neural network's (CNN) features, used within the context of the Maximum-Margin Object Detector (MMOD) (MMOD-CNN) face detector in the Dlib library (Dlib CNN), are adopted as the basis by which the faces and their positions within the object are detected, identified, and recognized [36][37][38]. The face within the object is extracted from the original headshot and converted to grayscale using OpenCV (Figure 3b).…”

Section: Sqlite Databasesmentioning

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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Feature Extraction Efficient for Face Verification Based on Residual Network Architecture

Cited by 2 publications

References 20 publications

Evaluating Secure Methodology for Photo Sharing in Online Social Networks

Evaluating Secure Methodology for Photo Sharing in Online Social Networks

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

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