There are 8 repositories under dlib topic.
To speedup and simplify image labeling/ annotation process with multiple supported formats.
Use Unity 3D character and Python deep learning algorithms to stream as a VTuber!
Real-time head pose estimation built with OpenCV and dlib
Creating a software for automatic monitoring in online proctoring
👦 Basic face landmarking on iPhone with Dlib via Swift & ObjC++
Nextcloud app that implement a basic facial recognition system.
Dlib .NET wrapper written in C++ and C# for Windows, MacOS, Linux and iOS
基于深度学习的驾驶员分心驾驶行为(疲劳+危险行为)预警系统使用YOLOv5+Deepsort实现驾驶员的危险驾驶行为的预警监测
Facial Landmark Detection and head pose compute use dlib, Real time Face Reconstruction use 3D Morphable Face Model fitting
👦 Fast-Face : Android App for Real-time Face Landmark Detection. You can check your landmarks in 60ms
A Free, Offline, Real-Time, Open-source web-app to assist organisers of any event in allowing only authorised/invited people using Face-Recognition Technology or QR Code.
Add Christmas hat on one's head based on OpneCV and Dlib
Attendance Management system using face recognition.
Command line Thug Meme generator written in Python
Face-recognition using Siamese network
Facial Recognition Pipeline using Dlib and Tensorflow
PSGAN running with ncnn⚡妆容迁移/仿妆⚡Imitation Makeup/Makeup Transfer⚡
Fast object detection, face recognition and S3 upload of ZoneMinder alarms.
😀 Live2D VTuber, made by Unity and Cubism. This project is based on TCP socket, OpenCV and deep learning
Android app to demo dlib face recognition
This project is an aid to the blind. Till date there has been no technological advancement in the way the blind navigate. So I have used deep learning particularly convolutional neural networks so that they can navigate through the streets.
C++17 templates between [stl::vector | armadillo | eigen3 | ublas | blitz++] and HDF5 datasets
Realtime Face Swap Android NDK app full source code. Developed with OpenCV (http://opencv.org) and Dlib C++ (http://dlib.net).
Realtime person's face recognize and can classify emotion using webcam, video or images.