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 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 guide walks through constructing a Python‑based real‑time face recognition pipeline using Dlib and OpenCV, covering dataset creation, feature extraction, Euclidean distance matching, and live video stream identification, complete with code snippets, parameter tuning tips, and troubleshooting advice.
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 guide introduces the open‑source Python library face_recognition, explains its high‑accuracy (up to 99.38%) facial detection and landmark capabilities, provides step‑by‑step code examples for locating faces, extracting landmarks, and comparing identities, and lists practical use‑case scenarios and the GitHub repository.
This article walks through creating a concise 200‑line Python script that automatically detects facial landmarks with dlib, aligns faces using Procrustes analysis, corrects color differences, and blends a second face onto a first image, complete with code snippets and step‑by‑step explanations.
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.
This tutorial walks you through setting up a simple face‑recognition pipeline using Dlib and scikit‑image in Python, covering required tools, data preparation, the recognition workflow, and a complete 40‑line script with sample images and execution results.
This article explains how to write a compact Python script (about 200 lines) that automatically detects facial landmarks with dlib, aligns two faces using Procrustes analysis, corrects color differences, and blends the second face onto the first using OpenCV, complete with full source code and visual examples.
This tutorial walks through setting up Ubuntu 17.10 with Python 2.7, installing required packages, compiling dlib, and using the face_recognition library to detect, identify, and beautify faces through multiple code examples.
This guide walks through setting up Ubuntu 17.10 with Python 2.7, installing dlib and the face_recognition library, and demonstrates five practical examples ranging from a one‑line face‑recognition command to facial feature extraction and beautification, complete with code snippets and screenshots.
This guide introduces the easy-to-use Python face_recognition library, explains its dlib‑based deep learning accuracy, details installation on various platforms, and demonstrates command‑line and Python API usage for detecting faces, facial landmarks, and real‑time recognition.
