Project 4 (Object Detection and Localization, Face Recognition)
Project 4 (Object Detection and Localization, Face Recognition)
Face Detection and Recognition System with Object Localization
Explanation:
In this project activity, you will develop a face detection and recognition system with object localization using the concepts covered in the lessons “Object Detection and Localization” and “Face Recognition.” The goal is to build a system that can accurately detect and recognize faces in images or video streams, while also localizing the detected faces within the images.
Steps:
- Dataset Selection: Choose a dataset that contains images or video frames with various faces. There are publicly available datasets like the Labeled Faces in the Wild (LFW) or the CelebA dataset that you can use for this project. Ensure that the dataset includes images with different lighting conditions, poses, and expressions.
- Preprocessing: Preprocess the images or video frames by resizing them to a consistent size and normalizing the pixel values. You can use image processing libraries like OpenCV for this step.
- Face Detection: Implement a face detection algorithm, such as the Viola-Jones algorithm or a deep learning-based approach using a pre-trained face detector model like Haar cascades or the Single Shot MultiBox Detector (SSD). Apply the face detection algorithm to the images or video frames to identify potential face regions.
- Object Localization: Utilize the face detection results to localize the detected faces within the images. You can draw bounding boxes around the detected face regions to visually indicate their locations.
- Face Recognition: Implement a face recognition algorithm, such as the Eigenfaces, Fisherfaces, or deep learning-based approaches using pre-trained models like FaceNet or VGGFace. Train the face recognition model using a subset of the dataset that includes labeled face images. The labeled images should have the corresponding identities associated with them.
- Face Classification: Apply the trained face recognition model to the detected face regions from Step 4. The model should classify the faces by assigning them the corresponding identities or labels from the training set. Evaluate the accuracy of the face recognition system by comparing the predicted identities with the ground truth labels.
- Visualization and Analysis: Visualize the results by displaying the original images with the localized face regions and the recognized identities. Calculate metrics such as accuracy, precision, and recall to assess the performance of the face detection and recognition system. Analyze any false detections or misclassifications to identify potential areas for improvement.
By completing this project activity, you will have gained practical experience in building a face detection and recognition system with object localization. This project provides a foundation for further exploration into advanced face detection and recognition techniques, as well as their applications in computer vision tasks involving face analysis, biometrics, and security systems.