IMPLEMENTATION OF LBPH ALGORITHM IN AUTOMATIC DOOR OPENING SYSTEM WITH FACIAL RECOGNITION
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Abstract
Automatic door opening system with facial recognition is an application that is widely used to improve security and comfort in various fields, such as offices, homes, and other limited areas. This study implements the Local Binary Pattern Histogram (LBPH) algorithm in an ESP32-based automatic door opening system integrated with an OV2640 camera. LBPH was chosen because of its superiority in recognizing faces even with varying lighting conditions and imperfect image quality. This system works by capturing facial images through an OV2640 camera, then processing the image using the LBPH algorithm to extract facial features. The results of facial recognition are then sent to the ESP32 which controls the automatic door locking system. In this implementation, the ESP32 is used as a microcontroller to connect the camera, data processing, and control the door actuator. Tests were conducted to measure the accuracy of facial recognition and system performance in opening the door. The test results show that the system can recognize faces with a high level of accuracy and open the door automatically in a short time, thereby increasing the efficiency and security of the system.
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2. Aryza, S, et al (2024) A ROBUST OPTIMIZATION TO DYNAMIC SUPPLIER DECISIONS AND SUPPLY ALLOCATION PROBLEMS IN THE MULTI-RETAIL INDUSTRY. Eastern-European Journal of Enterprise Technologies, (3).
3. Aryza, S., Wibowo, P., & Saputra, D. (2022, July). Rancang Bangun Alat Pengontrolan Proses Pemanasan Produksi Biodisel Dari Minyak Jelantah Berbasis Arduino Mega. In Prosiding Seminar Nasional Sosial, Humaniora, dan Teknologi (pp. 121-127).
4. Anisah, S., Fitri, R., Taro, Z., & Wijaya, R. F. (2022). Comparison of Lighting Efficiency (Led-CFL) based on Environmentally Friendly Technology. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 568-577.
5. Chen, Z., Li, S., Sun, X., & Zhang, Y. (2021). A comprehensive review on fast charging of lithium-ion batteries: Current status, challenges, and future perspectives. Journal of Energy Storage, 39, 102676.
6. Satria, B., & Fatahanan, I. (2024). Implementation Of Hydroponic Device Control System Via Website. Journal of Information Technology, computer science and Electrical Engineering, 1(3), 106-113.
7. Hamdani et al (2023) Kajian Pembangunan Lift Barang Pintar Kapasitas 50 Kg Dengan Pembangkit Listrik Tenaga Surya (PLTS). INTECOMS: Journal of Information Technology and Computer Science, 6(1), 429-433.
8. Li, M., Zhang, D., & Xu, H. (2020). Improving face recognition accuracy using LBPH and PCA on low-resolution images. International Journal of Computer Vision and Image Processing, 10(1), 53–67.https://doi.org/10.4018/IJCVIP.2020070104
9. Wang, L., & Shen, Z. (2021). Hybrid face recognition model combining LBPH and CNN for security applications. Journal of Artificial Intelligence and Soft Computing Research, 11(3), 127–136.https://doi.org/10.3745/JAI-2021-0132
10.Wang, X., Zhou, X., & Yu, H. (2018). A novel approach to face recognition based on Local Binary Patterns and SVM. Journal of Pattern Recognition Research, 11(1), 21–31.https://doi.org/10.1007/s43054-018-0011-2
11. Xu, L., Liu, F., & Zheng, Y. (2021). Face recognition system based on LBPH and cloud computing. Journal of Internet of Things and Big Data, 9(3), 75–84.https://doi.org/10.1007/s42360-021-00425-4
12.Zhang, T., Li, Y., & Zhu, Y. (2022). Face recognition using LBPH and deep learning for security applications in smart home systems. Smart Computing and Applications, 22(2), 50–57.https://doi.org/10.1007/s42060-022-0057-9
13. Zhao, W., Chellappa, R., & Phillips, P.J. (2020). Face recognition in real-world environments. Computer Vision and Image Understanding, 194, 102890.https://doi.org/10.1016/j.cviu.2020.102890
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