Implement Face Liveliness and Anti-Spoofing Mechanism

[priyanshu596]

Pull Request for DL-Simplified
Issue Title: Implement Face Liveliness and Anti-Spoofing Mechanism
Info about the related issue: This PR implements a face liveliness and anti-spoofing mechanism using deep learning models. The goal is to enhance the security of face authentication systems by distinguishing real faces from spoofed attempts.
Name: Priyanshu Singh
GitHub ID: priyanshu596
Email ID: [email protected]
Identify yourself: WOC 4.0 Contributor
Closes: #904

Describe the add-ons or changes you've made
Implemented a face liveliness detection model using XceptionNet trained on the LFASD dataset.
Applied data augmentation, k-fold cross-validation, weighted loss functions, and SMOTE to handle class imbalance issues.
Integrated Haar Cascade and Shape Predictor for face detection.
Enhanced the robustness of the model by fine-tuning hyperparameters and optimizing for real-time performance.
Created a Flask app to demonstrate real-time face anti-spoofing detection.
Type of change
New feature (non-breaking change which adds functionality)
Code style update (formatting, local variables)
This change requires a documentation update
How Has This Been Tested?
Tested on multiple real and spoofed face samples to evaluate model accuracy.
Performed cross-validation and analyzed model performance metrics like precision, recall, and F1-score.
Ensured the Flask app integrates seamlessly with the trained model for live detection.
Checklist:
My code follows the guidelines of this project.
I have performed a self-review of my own code.
I have commented my code, particularly wherever it was hard to understand.
I have made corresponding changes to the documentation.
My changes generate no new warnings.
I have tested the Flask application to ensure real-time functionality.

[github-actions]

Our team will soon review your PR. Thanks @priyanshu596 :)