#人脸识别#Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.
翻译 - 去实现Pico人脸检测库。
#人脸识别#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.
#人脸识别#An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)
翻译 - 嵌入式计算机视觉和机器学习库（CPU优化和物联网功能）
This is an official implementation of facial landmark detection for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
[CVPR 2018] Look at Boundary: A Boundary-Aware Face Alignment Algorithm
Four landmark detection algorithms, implemented in PyTorch.
#人脸识别#Python library for analysing faces using PyTorch
#计算机科学#Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
Tutorial for computer vision and machine learning in PHP 7/8 by opencv (installation + examples + documentation)
3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
#计算机科学#PyTorch implementation of "Super-Realtime Facial Landmark Detection and Shape Fitting by Deep Regression of Shape Model Parameters" predicting facial landmarks with up to 400 FPS
翻译 - PyTorch实施“通过形状模型参数的深度回归进行超实时面部地标检测和形状拟合”，预测具有高达400 FPS的面部界标
Implementation of PFLD For 68 Facial Landmarks By Pytorch
#人脸识别#使用OpenCV实现人脸关键点检测
[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition
#计算机科学#The authors' implementation of the "Neural Head Reenactment with Latent Pose Descriptors" (CVPR 2020) paper.
#计算机科学#A TensorFlow implementation of HRNet for facial landmark detection.
#人脸识别#Facial-Landmarks Detection based animating application similar to Apple-Animoji™
翻译 - 基于面部标记检测的动画应用程序类似于Apple-Animoji™
#计算机科学#A tool for precisely placing 3D landmarks on 3D facial scans based on the paper "Multi-view Consensus CNN for 3D Facial Landmark Placement"
This deep learning application can detect Facial Keypoints (15 unique points). They mark important areas of the face - the eyes, corners of the mouth, the nose, etc.