Foreground-segmentation-using-kmeans-AND-Face-recognization-using-KNN

Interactive Foreground Segmentation and Face Recognition

This repository contains two main ASSIGNMENTS:

• Interactive Foreground Segmentation Using K-Means Clustering
• Face Recognition Using K-Nearest Neighbors (K-NN) Each ASSIGNMENT involves detailed data preprocessing, algorithm implementation, and result analysis. Below are the instructions and descriptions for each project.

Assignment 1: Interactive Foreground Segmentation Using K-Means Clustering

Objective

Implement a basic version of the interactive image cut-out/segmentation approach called Lazy Snapping. The program uses K-Means Clustering to segment images into foreground and background based on user-provided seed pixels.

Libraries Used

time: To measure the processing time for segmentation. PIL (Image): For opening and manipulating images. matplotlib.pyplot (plt): To display the segmentation results. numpy: For numerical operations. cv2 (OpenCV): For image processing tasks like reading images, converting color spaces, and displaying images.

Solution

• Segmentation Mask Processing:

Create three versions of the input image: One with background pixels set to black. One with foreground pixels set to black. The original image.

• Lazy Snapping:

Utilize human annotations (foreground and background seed pixels) to compute a precise figure-ground segmentation.

• Optimization:

K-Means Optimization: Use Mini-batch K-Means for efficiency. Reduce Color Space Dimensionality. Parallelize K-Means computation for foreground and background classes. Results and Observations

• N Values:

Increasing N results in more refined segmentation but increases computational complexity. Lower N values provide coarse segmentation but reduce computation time.

• Comparison:

Varying N values and strokes impact segmentation consistency and robustness. Running the Scripts

Assignment 2: Face Recognition Using K-Nearest Neighbors (K-NN)

Objective

Implement a K-Nearest Neighbors classifier from scratch for face recognition using the CMU Pose, Illumination, and Expression (PIE) Dataset.

Libraries Used

• numpy: For numerical operations.
• matplotlib: For visualization.

Tasks

• Pre-process the Dataset:

Normalize each face image vector to unit length. Split the dataset into training and testing sets.

• Implement K-NN Classifier:

Use Euclidean and cosine similarity distance measures. Evaluate performance for different k-values (2, 5, 7, 11).

• Apply SVM and GaussianNB:

Compare accuracy with K-NN. Perform Dimensionality Reduction:

Visualize datasets in 3-D using PCA. Preprocessing Steps

• Normalization:

Divide each vector by its magnitude.

• Splitting Dataset:

Randomly select 150 images for training and 20 for testing for each subject. Results and Observations

• Accuracy Comparison:

           - SVM: Highest accuracy (~97.65%).
           - K-NN: Competitive accuracy (~96.08%).
  

GaussianNB: Lower accuracy (~75.69%).

• Distance Metrics:

        - Euclidean Distance: Effective for face recognition.
        - Cosine Distance: Less effective for this dataset.