Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
Facial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/887 |
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| author | Sebastian Pecolt Andrzej Błażejewski Tomasz Królikowski Igor Maciejewski Kacper Gierula Sebastian Glowinski |
| author_facet | Sebastian Pecolt Andrzej Błażejewski Tomasz Królikowski Igor Maciejewski Kacper Gierula Sebastian Glowinski |
| author_sort | Sebastian Pecolt |
| collection | DOAJ |
| description | Facial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday devices. This study focuses on the development and validation of a facial recognition system utilizing a Haar cascade classifier and the AdaBoost machine learning algorithm. The system leverages characteristic facial features—distinct, measurable attributes used to identify and differentiate faces within images. A biometric facial recognition system was implemented on a Raspberry Pi microcomputer, capable of detecting and identifying faces using a self-contained reference image database. Verification involved selecting the similarity threshold, a critical factor influencing the balance between accuracy, security, and user experience in biometric systems. Testing under various environmental conditions, facial expressions, and user demographics confirmed the system’s accuracy and efficiency, achieving an average recognition time of 10.5 s under different lighting conditions, such as daylight, artificial light, and low-light scenarios. It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. Threshold optimization tests with six male participants revealed a value that effectively balances accuracy and efficiency. While the system performed effectively under controlled conditions, challenges such as biometric similarities and vulnerabilities to spoofing with printed photos underscore the need for additional security measures, such as thermal imaging. Potential applications include access control, surveillance, and statistical data collection, highlighting the system’s versatility and relevance. |
| format | Article |
| id | doaj-art-7488cd1fb88d4e5985491c574e80e68e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7488cd1fb88d4e5985491c574e80e68e2025-01-24T13:21:12ZengMDPI AGApplied Sciences2076-34172025-01-0115288710.3390/app15020887Personal Identification Using Embedded Raspberry Pi-Based Face Recognition SystemsSebastian Pecolt0Andrzej Błażejewski1Tomasz Królikowski2Igor Maciejewski3Kacper Gierula4Sebastian Glowinski5Faculty of Mechanical Engineering and Power Engineering, Koszalin University of Technology, Sniadeckich 2, 75453 Koszalin, PolandFaculty of Mechanical Engineering and Power Engineering, Koszalin University of Technology, Sniadeckich 2, 75453 Koszalin, PolandFaculty of Mechanical Engineering and Power Engineering, Koszalin University of Technology, Sniadeckich 2, 75453 Koszalin, PolandFaculty of Mechanical Engineering and Power Engineering, Koszalin University of Technology, Sniadeckich 2, 75453 Koszalin, PolandFaculty of Mechanical Engineering and Power Engineering, Koszalin University of Technology, Sniadeckich 2, 75453 Koszalin, PolandInstitute of Health Sciences, Slupsk Pomeranian University, Westerplatte 64, 76200 Slupsk, PolandFacial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday devices. This study focuses on the development and validation of a facial recognition system utilizing a Haar cascade classifier and the AdaBoost machine learning algorithm. The system leverages characteristic facial features—distinct, measurable attributes used to identify and differentiate faces within images. A biometric facial recognition system was implemented on a Raspberry Pi microcomputer, capable of detecting and identifying faces using a self-contained reference image database. Verification involved selecting the similarity threshold, a critical factor influencing the balance between accuracy, security, and user experience in biometric systems. Testing under various environmental conditions, facial expressions, and user demographics confirmed the system’s accuracy and efficiency, achieving an average recognition time of 10.5 s under different lighting conditions, such as daylight, artificial light, and low-light scenarios. It is shown that the system’s accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address challenges such as extreme facial angles. Threshold optimization tests with six male participants revealed a value that effectively balances accuracy and efficiency. While the system performed effectively under controlled conditions, challenges such as biometric similarities and vulnerabilities to spoofing with printed photos underscore the need for additional security measures, such as thermal imaging. Potential applications include access control, surveillance, and statistical data collection, highlighting the system’s versatility and relevance.https://www.mdpi.com/2076-3417/15/2/887AdaBoost algorithmbiometric featuresfacial recognitionHaar classifiermachine learningsecurity system |
| spellingShingle | Sebastian Pecolt Andrzej Błażejewski Tomasz Królikowski Igor Maciejewski Kacper Gierula Sebastian Glowinski Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems Applied Sciences AdaBoost algorithm biometric features facial recognition Haar classifier machine learning security system |
| title | Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems |
| title_full | Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems |
| title_fullStr | Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems |
| title_full_unstemmed | Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems |
| title_short | Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems |
| title_sort | personal identification using embedded raspberry pi based face recognition systems |
| topic | AdaBoost algorithm biometric features facial recognition Haar classifier machine learning security system |
| url | https://www.mdpi.com/2076-3417/15/2/887 |
| work_keys_str_mv | AT sebastianpecolt personalidentificationusingembeddedraspberrypibasedfacerecognitionsystems AT andrzejbłazejewski personalidentificationusingembeddedraspberrypibasedfacerecognitionsystems AT tomaszkrolikowski personalidentificationusingembeddedraspberrypibasedfacerecognitionsystems AT igormaciejewski personalidentificationusingembeddedraspberrypibasedfacerecognitionsystems AT kacpergierula personalidentificationusingembeddedraspberrypibasedfacerecognitionsystems AT sebastianglowinski personalidentificationusingembeddedraspberrypibasedfacerecognitionsystems |