Face Recognition-Based Attendance System

Gode, Chetan; Khobragade, Atish; Thanekar, Chinmay; Thengadi, Ojas; Lakde, Kaushik

doi:10.1007/978-981-19-9819-5_12

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Through this technology, employee identification can be done quickly and accurately, reducing the risk of errors in data recording and manipulation. According to Gode [33], the accuracy of face recognition systems reaches a very high level, even in less-than-ideal lighting conditions or changes in a person's physical appearance. Despite having various advantages, the use of face recognition systems in attendance also faces certain challenges.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Data Analytics in Attendance Systems: Unveiling Machine Learning Techniques, Patterns, Practices, and Emerging Trends

Santoso,

Manongga,

Setyawan

et al. 2024

sji

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Purpose: The research aims to identify patterns and trends in attendance management through the application of reward and punishment systems as innovative solutions for improving employee attendance and well-being. Methods: This research utilizes a descriptive analysis approach with the application of Machine Learning (ML) techniques to enhance the accuracy of attendance pattern prediction and ML models for the classification of emerging trends and patterns. Research data were obtained through the company's attendance system and divided into two segments (80% for training and 20% for testing) while maintaining a balanced class proportion, then processed using SPSS and Python software with the Scikit-learn library. Result: The results of the study show that employee attendance is increased from 86.52% to 90.44% when the reward and punishment method is applied to the employee attendance system. Proper reward allocation can increase employee motivation to adhere to work schedules and consistently attend, while punishment tends to lead to lower attendance rates. Novelty: This research emphasizes the optimization of attendance management through data analytics approaches and the implementation of advanced technology in attendance systems with the application of ML techniques to analyze attendance data comprehensively and detect significant patterns.

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Section: Introductionmentioning

confidence: 99%

“…One of them is privacy and data security issues. Research by [29], [33], [34], [35], [36], [37], [38] highlight issues related to the collection and use of sensitive biometric data on employee faces. This raises ethical questions about how the data is stored, accessed, and protected from misuse or security breaches.…”

Section: Introductionmentioning

confidence: 99%