Advanced Facial Recognition Systems for Real-Time Embedded Applications
This article explores several classical techniques of facial recognition, assessing their suitability for real-time embedded systems such as digital cameras. It also includes a thorough discussion on the training requirements for various facial recognition methods. Addressing mounting concerns in se...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00109.pdf |
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| _version_ | 1832098552378032128 |
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| author | Guerbaoui Mohammed Misbah Nouhaila Selmani Abdelouahed El Faiz Samira Benhala Bachir Ed-Dahhak Abdelali Lachhab Abdeslam |
| author_facet | Guerbaoui Mohammed Misbah Nouhaila Selmani Abdelouahed El Faiz Samira Benhala Bachir Ed-Dahhak Abdelali Lachhab Abdeslam |
| author_sort | Guerbaoui Mohammed |
| collection | DOAJ |
| description | This article explores several classical techniques of facial recognition, assessing their suitability for real-time embedded systems such as digital cameras. It also includes a thorough discussion on the training requirements for various facial recognition methods. Addressing mounting concerns in security and surveillance, this project aims to develop an advanced facial recognition system using a 2D detection approach. Leveraging the Intel RealSense 415 camera connected to a Raspberry Pi 3. An introduction to Haar Cascading models is provided, highlighting their advantages, particularly their ability to ensure acceptable levels of accuracy for facial recognition in unseen image collections. Lastly, the article offers a detailed description and implementation of a functional platform, accompanied by preliminary results. |
| format | Article |
| id | doaj-art-d1c187cd96b94bc3a8bc602b84a0e719 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-d1c187cd96b94bc3a8bc602b84a0e7192025-02-05T10:47:16ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010010910.1051/e3sconf/202560100109e3sconf_icegc2024_00109Advanced Facial Recognition Systems for Real-Time Embedded ApplicationsGuerbaoui Mohammed0Misbah Nouhaila1Selmani Abdelouahed2El Faiz Samira3Benhala Bachir4Ed-Dahhak Abdelali5Lachhab Abdeslam6MMCS Team, EST Meknes, Moulay Ismail UniversityFaculty of Sciences Meknes, Moulay Ismail UniversityS.A.R.S Team, ENSA of Safi, UCA UniversityMMCS Team, EST Meknes, Moulay Ismail UniversityFaculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah UniversityMMCS Team, EST Meknes, Moulay Ismail UniversityMMCS Team, EST Meknes, Moulay Ismail UniversityThis article explores several classical techniques of facial recognition, assessing their suitability for real-time embedded systems such as digital cameras. It also includes a thorough discussion on the training requirements for various facial recognition methods. Addressing mounting concerns in security and surveillance, this project aims to develop an advanced facial recognition system using a 2D detection approach. Leveraging the Intel RealSense 415 camera connected to a Raspberry Pi 3. An introduction to Haar Cascading models is provided, highlighting their advantages, particularly their ability to ensure acceptable levels of accuracy for facial recognition in unseen image collections. Lastly, the article offers a detailed description and implementation of a functional platform, accompanied by preliminary results.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00109.pdf |
| spellingShingle | Guerbaoui Mohammed Misbah Nouhaila Selmani Abdelouahed El Faiz Samira Benhala Bachir Ed-Dahhak Abdelali Lachhab Abdeslam Advanced Facial Recognition Systems for Real-Time Embedded Applications E3S Web of Conferences |
| title | Advanced Facial Recognition Systems for Real-Time Embedded Applications |
| title_full | Advanced Facial Recognition Systems for Real-Time Embedded Applications |
| title_fullStr | Advanced Facial Recognition Systems for Real-Time Embedded Applications |
| title_full_unstemmed | Advanced Facial Recognition Systems for Real-Time Embedded Applications |
| title_short | Advanced Facial Recognition Systems for Real-Time Embedded Applications |
| title_sort | advanced facial recognition systems for real time embedded applications |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00109.pdf |
| work_keys_str_mv | AT guerbaouimohammed advancedfacialrecognitionsystemsforrealtimeembeddedapplications AT misbahnouhaila advancedfacialrecognitionsystemsforrealtimeembeddedapplications AT selmaniabdelouahed advancedfacialrecognitionsystemsforrealtimeembeddedapplications AT elfaizsamira advancedfacialrecognitionsystemsforrealtimeembeddedapplications AT benhalabachir advancedfacialrecognitionsystemsforrealtimeembeddedapplications AT eddahhakabdelali advancedfacialrecognitionsystemsforrealtimeembeddedapplications AT lachhababdeslam advancedfacialrecognitionsystemsforrealtimeembeddedapplications |