PowerPoint Presentation

[Audio] Good day everyone. Today, we are presenting our project titled Multi-Pose Human Face Matching System Using Deep Learning and YOLO-V5. Our team comprises M. Karthik, S. Manikandan, M. Vicky, and M. Sethuram, under the guidance of Ms. S. Priya from the Computer Science and Engineering Department of Nehru Institute of Engineering and Technology..

[Audio] The objective of our project is to address challenges in human face matching across varying poses, which is crucial in applications like security and identity verification. We introduce a deep learning-based system using the YOLO-V5 algorithm to efficiently detect and match faces across multiple orientations..

[Audio] Our primary goals include developing a system capable of recognizing faces in multiple orientations, ensuring robustness in face detection, and improving real-time performance to reduce errors and enhance adaptability..

[Audio] Traditional face recognition systems often struggle with pose variations. To overcome these limitations, we propose a deep learning-based system integrated with YOLO-V5 and MATLAB image processing to detect and recognize faces from various angles..

[Audio] Existing systems rely heavily on generative adversarial networks or GANs. While they can generate frontal views from multi-pose inputs, they suffer from training difficulties, mode collapse, inconsistency, and potential overfitting..

[Audio] These drawbacks limit their real-world applicability..

[Audio] Our proposed system leverages YOLO-V5 for faster and more accurate face detection. By combining MATLAB's preprocessing capabilities with deep learning models,.

[Audio] We enhance accuracy even under challenging conditions such as occlusions or lighting changes..

[Audio] This slide illustrates the system architecture. It outlines how image acquisition, preprocessing, and face detection modules interact within our framework to support real-time multi-pose recognition..

[Audio] The system includes several hardware components such as the power supply unit, Arduino Uno, APR voice module, and speaker. Each part plays a role in either image acquisition, signal processing, or output delivery.

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[Audio] For our software stack, we used MATLAB 2014a, focusing on its Image Processing Toolbox to support data preparation, feature extraction, and model interfacing..

[Audio] Our system modules include Image Acquisition, Pre-Processing, and Image Analysis. Image acquisition converts real-world visuals into digital data. Preprocessing enhances image quality, and image analysis extracts meaningful patterns for recognition..

[Audio] In conclusion, our system achieves accurate face recognition across various poses using YOLO-V5 and MATLAB. It addresses limitations in existing methods and holds promise for applications in security and biometric verification..

[Audio] We have referred to several research publications in the areas of deep learning, face recognition, and image processing, which have significantly informed and validated our approach..

[Audio] Thank you for your attention. We welcome any questions you may have regarding our project.