Attendance and Security System Based on Building Video Surveillance

Sun, Kailai; Zhao, Qianchuan; Zou, Jianhong; Ma, Xiaoteng

doi:10.1007/978-981-13-6733-5_14

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…While coming into the scholars to the examination corridor for examination, they may be investigated well earlier than doing their tasks. 2018), presented a device [4] that integrates both the attendance and safety capabilities and fuses video picture processing, deep mastering, and face reputation to layout an clever attendance and safety device. It proposes a sliding common technique to become aware of men and women' identities.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Sathya¹,

Priya²,

Dhanalakshmi³

2022

ECS Trans.

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Face recognition aims to detect, track, identify, or verify person from video or images using recognition system. Its application varies from security, authentication, social media, and so on. With the advancement of technologies, still it impedes in accuracy. The challenges involved in face recognition are variations in facial expression involve diverging light, facial changes interfered with facial hair, pose, and so on. This research work presents a new hybrid approach, which improves the accuracy in identification of a person. The accuracy of the proposed work clearly depicts that it can be best suited in real time environments for tracking persons. It can be used for applications like authentication, security, and so on. The proposed work is validated by tracking the student presence. It captures the image of the student and detect the face by pre-processing, extracting the features and recognizing the person. For recognition it uses Local Binary Pattern Histogram (LBPH) algorithm and Haar Cascade classifier. LBP Algorithm uses Histogram, which improves the accuracy of the face recognition. It is integrated with Haar classifier, which uses machine learning algorithm. Adaboost Learning algorithm, which selects small features from large set, which improves the efficiency of recognition.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Sathya¹,

Priya²,

Dhanalakshmi³

2022

ECS Trans.

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“…Open-set recognizable proof is substantially more mind boggling as it needs to dismiss both unclear characters and fake acknowledges from the face identifier. A smart and secured attendance system was proposed in [12]. The method was built on networked surveillance video.…”

Section: Literature Surveymentioning

confidence: 99%

Security Monitoring System Using FaceNet For Wireless Sensor Network

Preetha¹,

Sheela²

2021

Preprint

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Face Recognition techniques have been rising rapidly over past few decades and gaining attraction in many security systems. Several methodologies are proposed to provide excellent results in face detection. Sensing technology enables several applications such as transportation, healthcare and security. Video surveillance systems trace and identify moving objects. Wireless Sensor networks are used to monitor remote areas and can be applied to monitor a facility by considering each camera as sensor nodes. Cameras are used as nodes in a wireless sensor network with a central server or a gateway node for all the monitoring and analysis of the information retrieved from the nodes. Identification and authentication of users in any organization is quite difficult due to high movement. Face recognition can be used detect faces and identify them continuously in a video feed which can be deployed to continuously monitor an area. Feeding from camera to base station uses Multi-task Cascaded Convolutional Neural Networks (MCTNN) and FaceNet algorithms for face recognition. Further information about the person is sent to all the end-user nodes present in the wireless network. This approach has been implemented and evaluated on a prototype wired camera network called FaceNet to monitor a network and provide security. A method for tracking people in 2D world coordinates and acquiring canonical frontal face images that fits the sensor network paradigm. The approach evaluates and demonstrates the tasking algorithm in action on data acquired from the FaceNet camera network.

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“…In recent years, deep neural networks such as convolutional neural networks (CNNs) [1,2,3] and transformer [4] have been successfully applied to object detection tasks. People detection is one of the most important research topics in the object detection field, which is a fundamental component in many applications [5], such as autonomous driving [6], people identification [7], intelligent video surveillance [8], etc. People detection methods are mainly divided into three categories: body, face, and head detection.…”

Section: Introductionmentioning

confidence: 99%

MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings

Sun¹,

Ma²,

Liu³

et al. 2021

Preprint

Self Cite

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Head detection in real-world videos is an important research topic in computer vision. However, existing studies face some challenges in complex scenes. The performance of head detectors deteriorates when objects which have similar head appearance exist for indoor videos. Moreover, heads have small scales and diverse poses, which increases the difficulty in detection. To handle these issues, we propose Motion-Guided Pseudo Siamese Network for Indoor Video Head Detection (MGPSN), an end-to-end model to learn the robust head motion features. MGPSN integrates spatialtemporal information on pixel level, guiding the model to extract effective head features. Experiments show that MG-PSN is able to suppress static objects and enhance motion instances. Compared with previous methods, it achieves stateof-the-art performance on the crowd Brainwash dataset. Different backbone networks and detectors are evaluated to verify the flexibility and generality of MGPSN.

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Attendance and Security System Based on Building Video Surveillance

Cited by 4 publications

References 8 publications

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Security Monitoring System Using FaceNet For Wireless Sensor Network

MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings

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