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S.Yoheswari, Nirmal Kmar M, Mohammed Thalha A, Maathesh N
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Page No: 567 - 574
Abstract : The core objective of this project is to recognize and reconstruct distorted facial images, particularly in the context of accidents. This involves using deep learning techniques to analyze the features of a distorted face and regenerate it into a recognizable form. Deep learning models are well-suited for this task due to their ability to learn complex patterns and representations from data the input data consists of distorted facial images, typically obtained from MRI scans of accident victims. These images may contain various types of distortions such as swelling, bruising, or other injuries that affect facial appearance. By using MRI images, the project can focus on medical applications where accurate facial reconstruction is crucial for diagnosis or identification purposes.Two common pre-trained deep learning models, VGG19 and 3D CNN, are chosen for this project. VGG19 is a convolutional neural network (CNN) known for its effectiveness in image classification tasks, while 3D CNNs are capable of capturing spatial and temporal features from volumetric data like MRI scans. By leveraging these pre-trained models, the project can benefit from their learned representations and potentially achieve better reconstruction accuracy. The performance of the deep learning models is evaluated using metrics such as accuracy and error rate. Accuracy measures how well the models are able to reconstruct the facial features compared to the original images, while the error rate indicates the frequency of incorrect reconstructions. By quantifying these metrics, the project can assess the effectiveness of each algorithm in reconstructing distorted faces. The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents.
Keyword: Convolutional Neural Network (CNN), VGG19, Deep Learning, MRI Images, Local Binary Pattern
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