Real-Time Safety and Surveillance System Using Facial Recognition Mechanism

Pandey, Sachi; Chouhan, Vikas; Mahapatra, Rajendra Prasad; Chhettri, Devansh; Sharma, Himanshu

doi:10.1007/978-981-15-5566-4_44

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Electronic bracelets are used in Hong Kong and China. In Poland and Singapore, they use specialized applications through which calls are made at random in time, (Pandey et al, 2021). In China, Spain, France, the United States, drones are used with visual surveillance systems and face recognition systems.…”

Section: Resultsmentioning

confidence: 99%

Digital Transformation Of Governance During The Pandemic And Respect For Human Rights

Fadeev¹,

Rudneva²

2021

Global Challenges and Prospects of the Modern Economic Development Proceedings of Global Challenges and Prospects of the Modern

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Section: Resultsmentioning

confidence: 99%

Digital Transformation Of Governance During The Pandemic And Respect For Human Rights

Fadeev¹,

Rudneva²

2021

Global Challenges and Prospects of the Modern Economic Development Proceedings of Global Challenges and Prospects of the Modern

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“…Pandey et al combined the on-demand resource availability of cloud computing, which can store and retrieve the captured video anytime. Additionally, its surveillance mechanism involves the Viola-Jones algorithm for face detection by analyzing captured data [28]. Shanthi and Svalakshmi developed a surveillance method by using a face recognition based unmanned aerial vehicle (UAV) [29].…”

Section: Introductionmentioning

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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“…Biometrics is described as a pattern-recognition technique for individual identification, based on their physical, chemical, or behavioral characteristics [ 1 , 2 ]. One of the most popular biometric techniques is face recognition [ 2 ], which has abundant applications [ 3 , 4 , 5 ] in various areas, such as: (1) security, including identity verification [ 6 , 7 ], computer or mobile device unlock [ 7 , 8 ], criminal records search, and voter registration; (2) surveillance, such as cameras used on closed circuit television (CCTV) [ 9 ]; and (3) access control that could grant access to a specific place or an electronic account to a group of people [ 10 ] using their faces as a credential. As described by Das et al [ 11 ], there is a growing attention from the scientific community on mobile devices with biometric recognition.…”

Section: Introductionmentioning

confidence: 99%

“…As described by Das et al [ 11 ], there is a growing attention from the scientific community on mobile devices with biometric recognition. This attention is mainly fueled by the commercial interest in robust authentication methods for smartphones, laptops, tablets, and other mobile devices [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela

Soto

Zarkesh-Ha

et al. 2021

Sensors

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In this paper, we present the architecture of a smart imaging sensor (SIS) for face recognition, based on a custom-design smart pixel capable of computing local spatial gradients in the analog domain, and a digital coprocessor that performs image classification. The SIS uses spatial gradients to compute a lightweight version of local binary patterns (LBP), which we term ringed LBP (RLBP). Our face recognition method, which is based on Ahonen’s algorithm, operates in three stages: (1) it extracts local image features using RLBP, (2) it computes a feature vector using RLBP histograms, (3) it projects the vector onto a subspace that maximizes class separation and classifies the image using a nearest neighbor criterion. We designed the smart pixel using the TSMC 0.35 μm mixed-signal CMOS process, and evaluated its performance using postlayout parasitic extraction. We also designed and implemented the digital coprocessor on a Xilinx XC7Z020 field-programmable gate array. The smart pixel achieves a fill factor of 34% on the 0.35 μm process and 76% on a 0.18 μm process with 32 μm × 32 μm pixels. The pixel array operates at up to 556 frames per second. The digital coprocessor achieves 96.5% classification accuracy on a database of infrared face images, can classify a 150×80-pixel image in 94 μs, and consumes 71 mW of power.

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Real-Time Safety and Surveillance System Using Facial Recognition Mechanism

Cited by 3 publications

References 5 publications

Digital Transformation Of Governance During The Pandemic And Respect For Human Rights

Digital Transformation Of Governance During The Pandemic And Respect For Human Rights

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

Face Recognition on a Smart Image Sensor Using Local Gradients

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