Automated Facial Recognition Attendance System Leveraging IoT Cameras

Dmello, Royston; Yerremreddy, Sai; Basu, Samriddha; Bhitle, Tejas; Kokate, Yash; Gharpure, Prachi

doi:10.1109/confluence.2019.8776924

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Applications: Speculative inference can benefit data-driven applications that involve the fusion of multi-modal data under latency constraints. Wirelessly networked distributed smart camera and microphone systems are common in security, automation, biometric and environmental applications [22,81]. Speculative inference is an effective way to fuse such multi-modal signals in real-time while being robust to bandwidth differences.…”

Section: Overview Of Speculative Inferencementioning

confidence: 99%

Low-latency speculative inference on distributed multi-modal data streams

Huang

Risinger

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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While multi-modal deep learning is useful in distributed sensing tasks like human tracking, activity recognition, and audio and video analysis, deploying state-of-the-art multi-modal models in a wirelessly networked sensor system poses unique challenges. The data sizes for different modalities can be highly asymmetric (e.g., video vs. audio), and these differences can lead to significant delays between streams in the presence of wireless dynamics. Therefore, a slow stream can significantly slow down a multi-modal inference system in the cloud, leading to either increased latency (when blocked by the slow stream) or degradation in inference accuracy (if inference proceeds without waiting). In this paper, we introduce speculative inference on multi-modal data streams to adapt to these asymmetries across modalities. Rather than blocking inference until all sensor streams have arrived and been temporally aligned, we impute any missing, corrupt, or partially-available sensor data, then generate a speculative inference using the learned models and imputed data. A rollback module looks at the class output of speculative inference and determines whether the class is sufficiently robust to incomplete data to accept the result; if not, we roll back the inference and update the model's output. We implement the system in three multi-modal application scenarios using public datasets. The experimental results show that our system achieves 7 − 128× latency speedup with the same accuracy as six state-of-the-art methods. CCS CONCEPTS• Computer systems organization → Cloud computing.

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Section: Overview Of Speculative Inferencementioning

confidence: 99%

Low-latency speculative inference on distributed multi-modal data streams

Huang

Risinger

et al. 2021

Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services

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“…Even though there was only one study that used a neural network for detection [11], LearnOpenCV.com proposes that DNN overcomes most of the drawbacks of Viola-Jones and as such is worth considering.…”

Section: Related Studiesmentioning

confidence: 99%

Semiautomated Class Attendance Monitoring Using Smartphone Technology

Cronjé

Sanders

2020

JAIT

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Class attendance is important. Class attendance recording is often done using ‘roll-call’ or signing attendance registers. These are time consuming, easy to cheat and it is difficult to draw any information from them. There are other, expensive alternatives to automate attendance recording with varying accuracy. This study experimented with a smart phone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully, while keeping the solution cost-effective. The effect of different class sizes was also investigated. The research was done within a pragmatism philosophy, using a prototype in a field experiment. The algorithms that were used, are: Viola-Jones (HAAR features), Deep Neural Network (DNN) and Histogram of Oriented Gradients (HOG) for detection and Eigenfaces, Fisherfaces and Local Binary Pattern Histogram (LBPH) for recognition. The best combination was Viola-Jones combined with Fisherfaces, with a mean accuracy of 54% for a class of 10 students and 34.5% for a class of 22 students. The best all over performance on a single class photo was 70% (class size 10). As is, this prototype is not accurate enough to use, but with a few adjustments, it may become a cheap, easy-to-implement solution to the attendance recording problem.

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“…In the recent works for providing reliable and time saving Internet Of Things (IOT) used to automatic attendance system as shown in (Salman et al, 2019). In another work (Chen and Li, 2020) used IOT for designing intelligent campus and smart system including attendance management system, also IOT with using biometric used to intelligent attendance system (Dmello et al, 2019;Mon et al, 2019;Srivastava et al, 2019;Al_Janabi, 2020;Vatwani et al, 2020). Finally, a number of systems proposed to control attendance system but still need to improve the system.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Attendance System Using Artificial Neural Network Based on Students’ Background

Abdullah¹,

Wakil²,

Al-Shatri³

2020

Journal of Computer Science

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Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this study, we propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for proposing an intelligent attendance system to calculate the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses, if not the students have low activity should taking high attendance. This system will more efficient for monitoring students in the classes and replacing absent to activity.

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Automated Facial Recognition Attendance System Leveraging IoT Cameras

Cited by 8 publications

References 8 publications

Low-latency speculative inference on distributed multi-modal data streams

Low-latency speculative inference on distributed multi-modal data streams

Semiautomated Class Attendance Monitoring Using Smartphone Technology

Intelligent Attendance System Using Artificial Neural Network Based on Students’ Background

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