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Developer Roadmap for Facial Recognition Using Singular Value Decomposition in MATLAB with HPC Integration

Phase 1: Setup and Familiarization

  1. Understand the Project Scope

    • Review the project objectives, methodology, and datasets.
    • Familiarize yourself with MATLAB's Parallel Computing Toolbox and HPC environment.
    • Study relevant SVD concepts and their application in image processing.

  2. Environment Setup

    • Install MATLAB and ensure access to the Parallel Computing Toolbox.
    • Set up HPC credentials and access configurations.
    • Clone or access tutorials/resources such as MATLAB MPI GitHub repository.
    • Test the MATLAB-HPC connection by running a sample parallelized script.

Phase 2: Dataset Preparation

  1. Dataset Collection

    • Download the Extended Yale B Dataset and Labeled Faces in the Wild (LFW).
    • Verify the integrity of the datasets.

  2. Data Preprocessing Pipeline

    • Write MATLAB scripts to:
      • Convert images to grayscale.
      • Resize all images to a fixed dimension (e.g., 128 × 128).
      • Normalize pixel values to [0, 1].
    • Test preprocessing scripts on a small subset of images.
    • Optimize preprocessing for parallel execution using MATLAB's parfor.

  3. Documentation

    • Create a clear, step-by-step guide for the dataset preparation process.

Phase 3: SVD Implementation

  1. Data Matrix Construction

    • Write a script to vectorize images and form the data matrix ( A ).
    • Confirm the dimensionality of ( A ) for a small dataset.

  2. Apply Singular Value Decomposition

    • Implement the SVD algorithm in MATLAB: A = U * Σ * V'.
    • Visualize and verify the eigenfaces (columns of ( U )).
    • Retain top ( k ) singular values and vectors for dimensionality reduction.

  3. Optimize SVD for HPC

    • Parallelize the SVD computation using the Parallel Computing Toolbox and HPC resources.
    • Use MATLAB's svds function for large datasets.
    • Benchmark execution time with and without parallelization.

Phase 4: Feature Extraction

  1. Reduced Representation

    • Use the top ( k ) weights from ( V^T ) to create feature vectors for each image.
    • Verify the dimensionality and quality of the reduced feature vectors.

  2. Save Processed Data

    • Save reduced feature vectors and metadata (e.g., labels) for training and testing.
    • Implement robust saving/loading mechanisms for efficient experimentation.

Phase 5: Supervised Classification

  1. Split Data

    • Divide the dataset into training and testing sets (e.g., 80-20 split).

  2. Model Selection and Implementation

    • Train a Support Vector Machine (SVM) classifier using fitcsvm.
    • Alternatively, implement k-Nearest Neighbors (kNN) using fitcknn.

  3. Validation and Testing

    • Evaluate model performance using metrics like accuracy.
    • Compare results for different values of ( k ) (dimensionality).
    • Visualize classification results using confusion matrices or other techniques.

Phase 6: Performance Evaluation

  1. Measure Metrics

    • Calculate:
      • Accuracy: Percentage of correctly classified faces.
      • Compression Ratio: Retained components to original dataset size.
      • Execution Time: Time for SVD and classification.

  2. Scalability Testing

    • Experiment with larger datasets or synthetic datasets to test HPC scalability.
    • Use additional HPC nodes to parallelize the workload further.

Phase 7: Extensions

  1. Compare with PCA

    • Implement PCA for dimensionality reduction and compare performance with SVD.

  2. Real-Time Recognition

    • Explore MATLAB’s webcam interface to perform live recognition using pre-computed features.
    • Optimize for low latency and high accuracy.

  3. Write a Report

    • Document the implementation process, results, and key insights.
    • Include visualizations like execution time comparisons, accuracy plots, and confusion matrices.

Phase 8: Presentation and Submission

  1. Create Presentation Slides

    • Summarize objectives, methodology, results, and conclusions.
    • Include visual aids like eigenfaces, performance metrics, and scalability results.

  2. Submit Project

    • Package the final MATLAB scripts, processed datasets, and report.
    • Verify reproducibility by including a README with clear instructions.

Timeline (Estimated)

Phase Duration
Phase 1: Setup 1 Week
Phase 2: Dataset Prep 1 Week
Phase 3: SVD 2 Weeks
Phase 4: Feature Extract 1 Week
Phase 5: Classification 1 Week
Phase 6: Evaluation 1 Week
Phase 7: Extensions 2 Weeks (Optional)
Phase 8: Presentation 2-3 Days