This tutorial explains how to create synthetic masked faces by using OpenCV and dlib to detect facial landmarks, draw various mask shapes, and overlay them on images, providing a practical pipeline for mask‑aware face recognition research.
This article introduces the open‑source Python library face_recognition, explains how to install it, locate and extract faces, generate 128‑dimensional embeddings, compare faces, detect facial landmarks, apply virtual makeup, and build a simple custom face‑recognition application with complete code examples and visual results.
This article demonstrates how to install the Python face_recognition library, locate faces in images, crop and save them, encode faces into 128‑dimensional vectors, compare faces, detect facial landmarks, apply virtual makeup, and assemble these steps into a simple custom face‑recognition application.
