Recognize faces Application

Our application uses C++ language that focuses on face detection and recognition using PCA/Eigen analysis. This project aims to provide robust and efficient methods for detecting and recognizing faces in color and grayscale images. Our application leverages the power of C++ to deliver high-performance algorithms and accurate results.

Project Features

In this project:

Face Detection:
- Our application employs advanced techniques to detect faces in images. It can analyze both color and grayscale images, making it versatile and adaptable to various scenarios. The face detection algorithm efficiently identifies facial features, enabling accurate localization of faces within the images.
Face Recognition with PCA/Eigen Analysis:
- Once the faces are detected, our application utilizes PCA/Eigen analysis for face recognition. This method extracts the essential facial features and represents them in a lower-dimensional space. By comparing these features, the application can recognize and match faces with a high degree of accuracy.
Performance Reporting:
- Our application provides comprehensive performance reports, allowing users to evaluate the effectiveness of the face detection and recognition algorithms. The reports include metrics such as precision, providing insights into the application's performance.
ROC Curve Plotting:
- In addition to performance metrics, our application generates Receiver Operating Characteristic (ROC) curves. These curves plot the true positive rate against the false positive rate at various thresholds, providing a visual representation of the algorithm's performance. ROC curves are widely used in evaluating the effectiveness of face recognition systems.

Project Structure

The Application is built using:

C++/Opencv:
- Opencv 14/15/16 versions
QT framework:
- QT 6.4.2 version

├─ GUI
│  ├─  Data/Test
│  ├─  PCA
│  ├─  ReadWrite
│  ├─  Recognition
│  ├─  TrainedData
│  ├─  TrainedModel
│  ├─  BackgroundImage
│  ├─  ImagesLists
│  ├─  Icons
│  ├─  Common
│  ├─  main
│  └─  mainwindow
├─ Model_Training
│  ├─  Detection
│  ├─  PCA_Reduced
│  ├─  ReadWrite_Files
│  ├─  Recognition
│  ├─  TrainedData
│  ├─  image_lists
│  ├─  images_script
│  ├─  main_PCA
│  └─  Common
README.md