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This article demonstrates how to create a Python script that automatically detects facial landmarks with dlib, aligns two images using Procrustes analysis, corrects color differences, and blends the faces together with OpenCV, providing complete code and step‑by‑step explanations.
This tutorial explains how to implement face, eye, and mouth detection using OpenCV's Haar cascades, Dlib's HOG and CNN detectors, and provides step‑by‑step code for both static images and real‑time video streams in Python.
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 guide walks through installing required libraries, preparing source and target images, detecting and aligning facial landmarks with dlib, swapping faces using OpenCV, displaying and saving the result, and adding a Flask‑based image‑upload interface to automate the one‑click face swap workflow.
This tutorial explains how to use Dlib and OpenCV in Python to capture face images, extract 128‑dimensional descriptors, store average features in CSV files, and perform real‑time face recognition by comparing Euclidean distances against a known dataset.
This tutorial demonstrates how to perform face swapping on static images using Python, dlib, and OpenCV, guiding readers through landmark detection, Procrustes alignment, color correction, and seamless blending to achieve realistic results.
This tutorial explains how to implement high‑accuracy face recognition using OpenCV, Python, and deep learning by leveraging dlib's deep metric learning, creating a custom dataset, encoding facial embeddings, and performing real‑time identification on images and video streams.