In car theft security system by face detection
| dc.contributor.advisor | Koyuncu, Hakan | |
| dc.contributor.author | Altaher, Raoof Hayder Raoof | |
| dc.date.accessioned | 2023-12-28T08:04:39Z | |
| dc.date.available | 2023-12-28T08:04:39Z | |
| dc.date.issued | 2023 | en_US |
| dc.date.submitted | 2023 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalı | en_US |
| dc.description.abstract | This thesis presents an innovative approach to Optimize Neural Networks for the sake of preventing cars thefts by utilizing biologic identification by deep machine learning. The proposed optimization technique leveraging the power of Yogi algorithm [43] adjusted with Nesterov Accelerated Gradient (Nesterov Momentum) [48] and weights averaging [42]. This approach can potentially enhance the performance leading to improved generalization, reduced overfitting, faster and enhanced convergence. The model employs a neural network architecture consist input layer (120, 120, 3) followed by hidden layers and followed by two prediction heads each one start with a Global Max Pooling with Rectified Linear Units (ReLU) and Dense layer with Sigmoid functions to perform facial Detection. A Siamese Network consists of three subnetworks trained to preform facial Recognition, this neural network uses a triple loss method, consists of three major components ( Embedding layer have three inputs each one followed by a neural network similar to ResNet50 architecture and output of Dense layer 256 units as a feature vector for the that input, A Distance layer subtracting the three Embeddings, And a Classification layer calculated the triplet loss function. Experiment held using Google Colab and the performance is evaluated through metrics such as regression losses, classification losses and IoU for the face detection neural network. And Loss, Accuracy and other positives and negatives calculations that evaluate the Siamese Neural network.The results demonstrate the efficacy of the proposed method in terms regression and classifications tasks in Deep neural networks, which can be later used for providing a novel reliable solution for optimizing neural networks. | en_US |
| dc.identifier.citation | Altaher, R. H. R. (2023). In car theft security system by face detection. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/4465 | |
| dc.identifier.yoktezid | 832427 | |
| dc.institutionauthor | Altaher, Raoof Hayder Raoof | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Deep Machine Learning | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Yogi Algorithm | en_US |
| dc.subject | Nesterov Momentum | en_US |
| dc.subject | Siamese Neural Networks | en_US |
| dc.subject | Triplet Loss | en_US |
| dc.subject | Face Detection | en_US |
| dc.title | In car theft security system by face detection | |
| dc.type | Master Thesis |
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