Face Mesh Using MediaPipe with Python OpenCV
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📌 Tutorial on Python Face Mesh (python OpenCV & MediaPipe package). In this video, we will do face detection and we will add face mesh to our face detection using OpenCV and MediaPipe package in a python programming language.
code: https://github.com/noorkhokhar99/Medi...
Overview
MediaPipe Face Mesh is a solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D facial surface, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance critical for live experiences.
Additionally, the solution is bundled with the Face Transform module that bridges the gap between face landmark estimation and useful real-time augmented reality (AR) applications. It establishes a metric 3D space and uses the face landmark screen positions to estimate a face transform within that space. The face transform data consists of common 3D primitives, including a face pose transformation matrix and a triangular face mesh. Under the hood, a lightweight statistical analysis method called Procrustes Analysis is employed to drive robust, performant, and portable logic. The analysis runs on a CPU and has a minimal speed/memory footprint on top of the ML model inference.
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