Abstract: With this study, deep learning and image processing algorithms, which bring different aspects to the security sector, are used and it is aimed to design a smart door lock system through facial recognition. The system, which will be designed to be completely contactless, allows the person to safely pass through images obtained only with the camera, using deep learning algorithms. The development board to be worked on in the study was used 4 Model B type of the Raspberry Pi development card, which is widely used in the market, and it was aimed to achieve the highest level of performance. During the development of this work, HAAR-Cascade and HoG (Histogram of Oriented Gradients), which are among the commonly used algorithms in the field of image processing, were used. As a result of the literature research, it has been shown that Keras VGG-Face based deep learning library produces stable and high performance and ResNet50 and VGG16 deep learning models are applied with different optimization parameters during training and how much they affect the result performances are compared and presented in tables.
erciyes üniversitesi bilimsel araştırma projeleri koordinatörlüğü
FYL-2022-12418
Abstract: With this study, deep learning and image processing algorithms, which bring different aspects to the security sector, are used and it is aimed to design a smart door lock system through facial recognition. The system, which will be designed to be completely contactless, allows the person to safely pass through images obtained only with the camera, using deep learning algorithms. The development board to be worked on in the study was used 4 Model B type of the Raspberry Pi development card, which is widely used in the market, and it was aimed to achieve the highest level of performance. During the development of this work, HAAR-Cascade and HoG (Histogram of Oriented Gradients), which are among the commonly used algorithms in the field of image processing, were used. As a result of the literature research, it has been shown that Keras VGG-Face based deep learning library produces stable and high performance and ResNet50 and VGG16 deep learning models are applied with different optimization parameters during training and how much they affect the result performances are compared and presented in tables.
FYL-2022-12418
Primary Language | Turkish |
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Subjects | Computer Vision and Multimedia Computation (Other) |
Journal Section | Articles |
Authors | |
Project Number | FYL-2022-12418 |
Publication Date | October 9, 2024 |
Submission Date | June 27, 2024 |
Acceptance Date | September 16, 2024 |
Published in Issue | Year 2024 Volume: 20 Issue: 1 |