Civil War Twin

Kusuma, Manisha; Mohanty, Vikram; Wang, Marx; Luther, Kurt

doi:10.1145/3514094.3534141

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…However, the study cautioned that unchecked use could push schools towards authoritarian and oppressive practices. Meanwhile, [24] approached the topic from a design perspective, emphasizing the ethical challenges of facial recognition. Through the development of an educational web application, "Civil War Twin," the study underlined the need for transparency, inclusivity, and empathy in designing such tools.…”

Section: Stakeholder Acceptance and Children Privacy Concernsmentioning

confidence: 99%

LunchByte: A Facial Recognition-Based Payment System for Health-Conscious Elementary Student Meals

Mirza,

Bitar,

Alfahid

et al. 2024

TEM Journal

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A significant number of elementary school students experience challenges related to lunch, including forgetting lunch money, lacking awareness of their individual dietary needs, and the importance of balanced nutritional choices for their overall health. Numerous parents and guardians struggle to monitor their children at school, control their purchasing behaviors, and maintain their health. Therefore, this research aims to enhance the efficiency and monitoring of elementary school students' lunch choices. LunchByte, a face recognition system developed in this study assists elementary in monitoring lunch expenses concerning their dietary restrictions. The main feature of LunchByte is using a student’s face print to generate a custom menu tailored to their allergies/diet restrictions and allowing them to pay with a prepaid balance added by their guardians. The evaluation of this system involves conducting model testing to assess the accuracy of facial recognition, integration testing to examine the interaction between different components of the system, and usability testing with end-users to evaluate the user experience. The results of these tests indicate that the system achieves high levels of accuracy and user satisfaction. The study is important because it has the potential to improve the overall health of young children.

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Section: Stakeholder Acceptance and Children Privacy Concernsmentioning

confidence: 99%

LunchByte: A Facial Recognition-Based Payment System for Health-Conscious Elementary Student Meals

Mirza,

Bitar,

Alfahid

et al. 2024

TEM Journal

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“…Face recognition is the process of identifying human face characteristics [1]. The face, a person's most recognizable biometric trait [2], is frequently employed in banking [3], biometric log-in, credit card verification, time attendance [4], video surveillance, public security [1], forensic identification [5], border surveillance [6], and military [7], etc. Today, it is considered one of the most important technologies that are in development [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Face Recognition Machine Learning Models

صفاء سالم محمد دخيلة,

نور الدين على احمد,

هالة الشاعري

2022

JOPAS

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Machine learning (ML) is one of the fastest-developing topics today, straddling the boundary between statistics and computer science, as well as data science. It is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. And It addresses the difficulty of the way to assemble gadgets that enhance themselves via experience, and make conclusions with minimum human assistance. For this purpose, there arises a need to use various statistical methods of face recognition’ models, such as (DeepFace) and (OpenFace). DeepFace is the most lightweight face recognition and facial attribute analysis library for Python, and is currently on the verge of human-level precision. OpenFace on the other hand is an open source deep learning facial recognition model based on Google's Facenet model. In this paper, we will discuss the face recognition comparison between two models DeepFace and OpenFace on the calibrators of (Accuracy, Error Rate and Verification Time). DeepFace showed a higher accuracy rate by (3%) than that of OpenFace, and a lower error rate by (3%). Whereas OpenFace delivered with a minimum time shorter than that of DeepFace by (0.061323) second.

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