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http://elartu.tntu.edu.ua/handle/lib/34232| Tytuł: | Розумний відео-дзвінок на основі Raspberry Pi |
| Inne tytuły: | Smart door-bell based on Raspberry PI |
| Authors: | Aboulfadel, Mohamed |
| Affiliation: | Тернопільський національний технічний університет імені Івана Пулюя |
| Bibliographic description (Ukraine): | Абуальфадел М. Розумний відео-дзвінок на основі Raspberry Pi / Абульфадел Мухамед // ТНТУ, ФІС, Тернопіль, 2021. 76 с. |
| Bibliographic description (International): | Aboulfadel M. Smart door-bell based on Raspberry PI /Aboulfadel Mohamed// Ternopil Ivan Puluj National Technical University, Faculty of Computer Information Systems and Software Engineering //Ternopil, 2021 // p.76 |
| Data wydania: | sty-2021 |
| Data archiwizacji: | sty-2021 |
| Date of entry: | 15-lut-2021 |
| Wydawca: | Тернопільський національний технічний університет імені Івана Пулюя |
| Kraj (kod): | UA |
| Place edycja: | Тернопільський національний технічний університет імені Івана Пулюя |
| Promotor: | Луцків, Андрій Мирославович Lutskiv, Andriy |
| Członkowie Komitetu: | Боднарчук, Ігор Орестович Bodnarchuk, Ihor |
| UDC: | 004.4 |
| Słowa kluczowe: | Raspberry Pi OpenCV Python real-time face recognition |
| Strony: | 76 |
| Abstract: | Being a student of Computer Engineering gave me a chance to choose this project where im going to explain how I made a Smart Doorbell with Face recognition real-time. Using the Raspberry PI 3 model B+ and PI Camera. In The software part I used Python programing language to write my code and SQL for the Database. |
| Content: | Introduction. 1. Analysis of subject area. 2. Hardware components the smart door-bell with real-time face recognition. 3. Software smart door-bell with real-time face recognition. 4. Occupational safety and health. Conclusions |
| URI: | http://elartu.tntu.edu.ua/handle/lib/34232 |
| Wykaz piśmiennictwa: | 1. A. J. Colmenarez and T. S. Huang, “Face detection and recognition,” NATO ASI Series F Computer and Systems Sciences, vol. 11, no. 2, pp. 208–218, 1998. 2 .T. Kondo and H. Yan, “Automatic human face detection and recognition under non-uniform illumination,” Pattern Recognition, vol. 32, no. 10, pp. 1707–1718, 1999 3. L. H. Koh, S. Ranganath, and Y. V. Venkatesh, “An integrated automatic face detection and recognition system,” Pattern Recognition the Journal of the Pattern Recognition Society, vol. 35, no. 6, pp. 1259–1273, 2002. 4. S. Chaudhry and R. Chandra, “Face detection and recognition in an unconstrained environment for mobile visual assistive system,” Applied Soft Computing, vol. 53, pp. 168–180, 2017. 5. M. H. Siddiqi, R. Ali, A. M. Khan, E. S. Kim, G. J. Kim, and S. Lee, “Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and non-linear feature selection,” Multimedia Systems, vol. 21, no. 6, pp. 541–555, 2015. 6. S. Zhang, X. Zhu, Z. Lei, X. Wang, H. Shi, and S. Z. Li, “Detecting face with densely connected face proposal network,” Neurocomputing, vol. 284, pp. 119–127, 2018. 7. S. Madhavan and N. Kumar, “Incremental methods in face recognition: a survey,” Artificial Intelligence Review, vol. 284, no. 5, pp. 119–127, 2019. 8. Y. Zhang, Y. Huang, S. Yu, and L. Wang, “Cross-view gait recognition by discriminative feature learning,” IEEE Transactions on Image Processing, vol. 99, 9. H. Shao, S. Chen, J. Zhao, W. Cui, and Y. U. Tianshu, “Face recognition based on subset selection via metric learning on manifold,” Frontiers of Information Technology & Electronic Engineering, vol. 16, no. 12, pp. 102–118, 2015. 10. A. K. Bobak, A. J. Dowsett, and S. Bate, “Solving the border control problem: evidence of enhanced face matching in individuals with extraordinary face recognition skills,” PLoS One, vol. 11, no. 2, Article ID e0148148, 2016. 11. Z. Lu, X. Jiang, and A. Kot, “Feature fusion with covariance matrix regularization in face recognition,” Signal Processing, vol. 144, pp. 296–305, 2018. 12. B. Samik and D. Sukhendu, “Mutual variation of information on transfer-CNN for face recognition with degraded probe samples,” Neurocomputing, vol. 310, pp. 299–315, 2018. 13. A. Rikhtegar, M. Pooyan, and M. T. Manzuri-Shalmani, “GA-optimized structure of cnn for face recognition applications,” IET Computer Vision, vol. 10, no. 6, pp. 559–566, 2016. 14. Y. X. Yang, C. Wen, K. Xie, F. Q. Wen, G. Q. Sheng, and X. G. Tang, “Face recognition using the SR-CNN model,” Sensors, vol. 18, no. 12, p. 1, 2018. 15. Computer-associated health complaints and sources of ergonomic instructions in computer-related issues among Finnish adolescents: A cross-sectional study 16.Paula T Hakala, Lea A Saarni, Ritva L Ketola, Erja T Rahkola, Jouko J Salminen & Arja H Rimpelä BMC Public Health volume 10, Article number: 11 (2010) |
| Typ zawartości: | Bachelor Thesis |
| Występuje w kolekcjach: | 123 — Комп’ютерна інженерія (бакалаври) |
Pliki tej pozycji:
| Plik | Opis | Wielkość | Format | |
|---|---|---|---|---|
| Dipl_Aboulfadel-final.pdf | 2,54 MB | Adobe PDF | Przeglądanie/Otwarcie |
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