Face Recognition Based Identity Verification and Tracking System Design

Abstract

The use and importance of various authentication systems are increasing in order to enhance the security of data access processes and to enable authorized logins to work environments. In this study, a facial recognition-based authentication and personnel tracking system automation was developed. The developed system runs on a Raspberry Pi. Images obtained through the camera module are analyzed using facial recognition algorithms. The face detection algorithm was used to determine the location of human faces in the image captured by the camera. This process was performed using the face recognition library's HOG (Histogram of Oriented Gradients) based face detection method. The entry and exit times of identified individuals are automatically recorded, and this data can be viewed via a web-based interface. The system enables the successful generation of detailed reports on information such as employee entry and exit times and break durations. The developed system offers a low-cost, real-time, contactless, and IoT-compatible solution to enhance organizational/data security. The results of the study show that the developed system is applicable to employee tracking applications.

Keywords

• Facial recognition
• Identity verification
• Raspberry Pi
• Web-based systems
• Employee tracking system
• Embedded systems

Supporting Institution

TÜBİTAK

Project Number

TBTK-0164-7249, App ID: 1919B012411684

Ethical Statement

The system developed in this study is a prototype design and was tested using data from voluntary participants. Prior to the study, formal ethics committee approval was obtained from the Scientific Research and Publication Ethics Committee of Kırklareli University (E-35523585-199-167443). The facial images were used only to evaluate the system's performance. This biometric data was not used for any commercial purpose and was not shared with third parties. For a real-world deployment, it is necessary to get explicit user consent and store data according to data protection laws (such as KVKK/GDPR). In future works, we plan to add data encryption methods to make the system more secure.

Thanks

This study was supported under the TÜBİTAK 2209-A program. (TBTK-0164-7249, App ID: 1919B012411684)

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