Real-Time Image Processing: Face Recognition based Automated Attendance System in-built with “Two-Tier Authentication” Method

Mehta, Riddhi; Satam, Sidh; Ansari, Maaz; Samantaray, S. D.

doi:10.1109/icdse50459.2020.9310090

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…A unique "two-tier authentication" concept was introduced by Mehta et al [23]. This concept was designed to enhance system accuracy and incorporate a time allowance mechanism for students.…”

Section: Related Workmentioning

confidence: 99%

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

Kanawade,

Surve,

Khonde

et al. 2023

ISI

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In the realm of surveillance, closed-circuit television (CCTV) cameras serve as a vigilant watch over unfamiliar entities. However, the unpredictability of such entities necessitates continuous human monitoring, an endeavor prone to error and demanding of significant resources. The automation of this process through face recognition could alleviate these burdens, provided the system delivers high precision and rapid judgment capabilities. This study presents a novel solution to these challenges: an automated human recognition and verification surveillance system, founded on a max-voting ensemble method. This innovative approach amalgamates five influential feature extraction models: VGGFace, FaceNet, FaceNet-512, Dlib, and Arcface, with a support vector machine deployed for classification. The proposed system was subjected to rigorous testing on the AT&T, faces94, Grimace, Georgia Tech, and FaceScrub datasets, demonstrating an impressive accuracy of 100% on the AT&T, faces94, and Grimace datasets, and 99.3% and 98% on the Georgia Tech and FaceScrub datasets, respectively. The system's performance was further enhanced through a re-verification technique, which facilitated swift and precise prediction of unknown entities in real time. This study thus contributes a significant advancement to the field of automated surveillance, offering a potent tool for efficient, accurate human recognition.

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Section: Related Workmentioning

confidence: 99%

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

Kanawade,

Surve,

Khonde

et al. 2023

ISI

View full text Add to dashboard Cite

show abstract

“…Mehta et al [48] have presented a development of automated assistance systems based on facial recognition. The relative study was performed to choose suitable detection, classified approaches as Faster R-CNN as well as Support Vector Classification outperformed its equivalent competitors.…”

Section: Literature Reviewmentioning

confidence: 99%

Human emotion recognition based on block patterns of image and wavelet transform

2021

IJATEE

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Automatic Attendance Monitoring System (AAMS)

Pandey

Verma

et al. 2022

Applications of Computational Methods in Manufacturing and Product Design

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Real-Time Image Processing: Face Recognition based Automated Attendance System in-built with “Two-Tier Authentication” Method

Cited by 6 publications

References 20 publications

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

Automated Human Recognition in Surveillance Systems: An Ensemble Learning Approach for Enhanced Face Recognition

Human emotion recognition based on block patterns of image and wavelet transform

Automatic Attendance Monitoring System (AAMS)

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