farhanashraf4/improvised-face-detection-and-recognition-from-video

Implemented multiple face detection algorithms to accurately count and save recognized faces in a designated folder, enhancing detection accuracy. Integrated ShuffleNet and MTCNN successfully. Developed intelligent graphics for project analysis in Excel. Implemented facial recognition using PCA and Eigenfaces for dataset matching.

Improvised Face Detection and Recognition from video Overview. This repository showcases expertise in face detection using advanced algorithms such as ShuffleNet and MTCNN for video processing. Additionally, the project includes face counting, data management, algorithm integration, and face recognition using Eigenfaces.

1. Face Detection Proficient in face detection using ShuffleNet and MTCNN algorithms specifically tailored for videos. This ensures accurate and efficient detection of faces in various scenarios.

2. Face Counting and Data Management Developed a robust system capable of accurately counting detected faces and efficiently storing them in a designated folder. This feature facilitates easy retrieval and analysis of detected faces.

3. Algorithm Integration Successfully integrated ShuffleNet and MTCNN algorithms to enhance face detection accuracy. This combination leverages the strengths of each algorithm, resulting in improved overall performance.

4. Data Visualization Utilized Excel to create insightful graphs for project analysis. This includes visual representations of face detection results, counts, and other relevant metrics, enhancing the interpretability of the data.

5. Face Recognition with Eigenfaces Implemented facial recognition using Principal Component Analysis (PCA) and Eigenfaces. This advanced technique allows for accurate matching of faces within a dataset, expanding the capabilities of the system.

6. Skills MTCNN ShuffleNet Eigenface Recognition Principal Component Analysis (PCA) Machine Learning

How to Use Clone the repository: git clone https://github.com/farhanashraf4/Improvised-Face-Detection-and-Recognition-from-video.git Follow instructions in the project documentation for setup and usage. Feel free to explore and contribute to further advancements in face detection and recognition. Your feedback and contributions are highly appreciated.

Note: Ensure that you have the necessary dependencies installed as specified in the project documentation.

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