Add facial Landmark detection

[vishapraj]

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[github-actions]

Hello @vishapraj, Thank you for generating an issue to this project! Please wait while we get back to you.

[chirag-garg9]

Hey @akshitagupta15june I have worked on various computer vision problems and have a good experience with computer vision algorithms related to faces.I would be very happy to work on this issue and contribute to this awesome repo.
Thank You!

[AfifaSadiq]

Hello @akshitagupta15june,
Could you please assign me this issue. I am willing to work on this and previous experience in working with computer vision and mediapipe.

Additionally, could you please add a label to these issues - gssoc and a level label such as level1 / level2 /level3

Problem:
Currently, our system lacks the capability for facial landmark detection, hindering the implementation of features such as augmented reality applications. This feature is crucial for tasks like applying masks, glasses, or filters to faces in real-time.

Solution:
Integrate facial landmark detection using Mediapipe, an API by Google, along with OpenCV and Python. This solution will enable real-time detection of 468 facial landmarks, facilitating the development of augmented reality applications. Leveraging Mediapipe's pre-trained models, we can efficiently detect facial landmarks without the need for extensive training data or model development.

Alternatives:

1. Manual implementation of facial landmark detection algorithms, which can be time-consuming and less accurate compared to utilizing pre-trained models.
2. Using other facial landmark detection libraries or APIs, though Mediapipe offers a comprehensive solution with pre-trained models readily available.

Approach:

1. Research Mediapipe's facial landmark detection capabilities and integration with OpenCV.
2. Develop a Python script that interfaces with Mediapipe and OpenCV to perform real-time facial landmark detection.
3. Test the script with sample images and live video feed to ensure accuracy and real-time performance.
4. Document the implementation process, including any challenges faced and solutions adopted, for future reference and knowledge sharing.

Additional Context:
Facial landmark detection is a critical component for various applications, particularly in the realm of augmented reality. By integrating Mediapipe's capabilities with OpenCV and Python, we aim to provide a robust solution for real-time facial landmark detection, enabling the development of innovative AR experiences.