Real-time implementation of face recognition system

Borkar, Neel Ramakant; Kuwelkar, Sonia

doi:10.1109/iccmc.2017.8282685

Cited by 37 publications

(17 citation statements)

References 4 publications

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“…For instance, the face detection model proposed in [1] achieves a precision of 93% even when detecting multiple faces. Due to the advancement of facial detectors, numerous applications such as real-time face recognition systems [2], security surveillance systems [3], etc. have been developed.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Framework to Detect Face Masks from Video Footage

Joshi

Kanahasabai

et al. 2020

2020 12th International Conference on Computational Intelligence and Communication Networks (CICN)

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The use of facial masks in public spaces has become a social obligation since the wake of the COVID-19 global pandemic and the identification of facial masks can be imperative to ensure public safety. Detection of facial masks in video footages is a challenging task primarily due to the fact that the masks themselves behave as occlusions to face detection algorithms due to the absence of facial landmarks in the masked regions. In this work, we propose an approach for detecting facial masks in videos using deep learning. The proposed framework capitalizes on the MTCNN face detection model to identify the faces and their corresponding facial landmarks present in the video frame. These facial images and cues are then processed by a neoteric classifier that utilises the MobileNetV2 architecture as an object detector for identifying masked regions. The proposed framework was tested on a dataset which is a collection of videos capturing the movement of people in public spaces while complying with COVID-19 safety protocols. The proposed methodology demonstrated its effectiveness in detecting facial masks by achieving high precision, recall, and accuracy.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Framework to Detect Face Masks from Video Footage

Joshi

Kanahasabai

et al. 2020

2020 12th International Conference on Computational Intelligence and Communication Networks (CICN)

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“…In this research, we propose a framework for face recognition as illustrated in Figure 1. It composes two main parts: (1) Training stream and (2) Testing phase. In both flows, it composes the following main cascade steps: -Query comparison: in this task, many requirements sentences is stored in database.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Face recognition has been become attractive field in computer vision because of its wide applications such as identification [1], re-identification [2], control and automation [3], security( [4], [30]), surveillance [5] and so on. Thus, there are many researches have concentrated this problem.…”

Section: Introductionmentioning

confidence: 99%

Realtime Face Recognition for Intelligent Door System

2020

IJETER

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This paper focus on in-deep analysis of the majority of both hand-craft based and CNN-based that proposed in states-of-the-art for face detection and face recognition method. This issue has been researched for recent decades as it enjoys many practical applications such as entertainments, security, tracking, human-machine interaction and so on. However, this problem has been faced to many challenges such as complex background, illumination, face pose, states of face, fake face. Which lead to high time cost, and low accuracy system. In recent times, thanks to the cutting-edge technique that give impressive performance on both hand craft-based and deep learning-based method. There have been improvement in detecting face exploiting feature vectors. Therefore, in this research, we comparing face detection model such as Haar Cascade[6], Dlib [19] and YOLO [27]. Then, we consider and compare affects of methods when extract feature vectors. Besides, we try to examine on various classifications to obtain the suitable system. Evaluation results confirm that the best accuracy rate achieves at 97.83%. The proposed method suggests a feasible solution addressing technical issues in using hand craft-based face recognition. Moreover, we deploy a realtime intelligent door system that utilize face recognition results to automatically control door.

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“…On T and AT dataset of imageries the presentation of suggested algorithm is assessed. [4] In processor-based communiqué of a system controller a verification is a important subject. Using PCA algorithm, a technique for Scholar's Attendance System which will integrate with the face recognition technology is described in this paper.…”

Section: Literature Surveymentioning

confidence: 99%

Face Detection and Recognition for use in Campus Surveillance

Kranthikiran*,

Pulicherla

2020

IJITEE

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The study of image processing in today’s world and the booming possibility of building a smart classroom and a smarter campus with the help of aided vigilance and surveillance is slowly moving from a thought that can be considered for the future to an actual real-world implementation. In modern day schools and university campuses there is an increasing demand for a real-time monitoring and quick responding database that tracks the student activities. This is not always required but serves as a one-click system that handles average information searches and returns the list of fast action options that are available in tracking the campus activities that come in the purview of its span. In a diverse educational campus comprising of several branches and streams that share a single campus, there is a possible chance of intrusion and unauthorized entry which may lead to undesirable and unnecessary loss of intellectual property and manipulation of identity. In a particular academic unit, there would be a surveillance system that monitors and tracks these activities and offers to its privileged users a response in real-time. It can also be used to track the attendance of students in an automated fashion which leads to a digitized and paperless approach. It can be said that this will be implemented in its entirety to a campus unit. Face identification is an essential step in face recognition, in which one of the typical of a class and authoritative application in visual sensor network. Visual perception is one of the physical measurements based on secured features. Face identification is a demanding assignment, because it has to scan and match against a library of known faces. E.g. lighting condition, different posture, various kind of body languages.

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Real-time implementation of face recognition system

Cited by 37 publications

References 4 publications

Deep Learning Framework to Detect Face Masks from Video Footage

Deep Learning Framework to Detect Face Masks from Video Footage

Realtime Face Recognition for Intelligent Door System

Face Detection and Recognition for use in Campus Surveillance

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