face-landmarks

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.

Robust realtime face and facial landmark tracking on CPU with Unity integration

Face landmarks(fiducial points) detection benchmark

💎An easy-to-use PyTorch library for face landmarks detection: training, evaluation, inference, and 100+ data augmentations.🎉

Finds facial features such as face contour, eyes, mouth and nose in an image.

FaceLandmark Detector using Dlib (Unity Asset Plugin)

Using OpenCV and Dlib to predict facial attractiveness.

[CVPR 2020] MERL-RAV Dataset contains over 19k faces annotated with 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded or externally occluded.

Deep learning based gaze estimation demo with a fun feature :-)

This asset is an example project that maps face mask to the detected faces in an image using “OpenCV for Unity” and “Dlib FaceLandmark Detector”.

[CVPR 2020] Re-hosting of the LUVLi Face Alignment codebase. Please download the codebase from the original MERL website by agreeing to all terms and conditions. By using this code, you agree to MERL's research-only licensing terms.

Face recognition using dlib and kNN classification (ROS compatible)

Face detection and recognition library that focuses on speed and ease of use.

A pytorch version of face landmark framework

HoloLens With DlibFaceLandmarkDetector Example (Support for Hololens1 and Hololens2)

VisionFace: All-in-one face analysis framework: detection, recognition, embeddings, landmarks, anti-spoofing and support visualization

This asset is an example of swapping two faces in an image using “OpenCV for Unity” and “Dlib FaceLandmark Detector”.

Code to generate G_seg, G_pos and G_app images from "Example-Based Synthesis of Stylized Facial Animations" by JAKUB FIŠER et al.