Face Analysis Project on MXNet
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Updated
Aug 16, 2020 - Python
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Face Analysis Project on MXNet
The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.
papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval;
Deep Learning Based Free Mobile Real-Time Face Landmark Detector.
Implementations of PCN (an accurate real-time rotation-invariant face detector) and other face-related algorithms
[CVPR 2018] Look at Boundary: A Boundary-Aware Face Alignment Algorithm
This repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.
TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes SDK On Mobile.
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
A deep neural network for face alignment
Estimate 3D face pose (6DoF) or 11 parameters of 3x4 projection matrix by a Convolutional Neural Network
3D face swapping implemented in Python
A simple face detect and alignment method, which is easy and stable.
Fast and accurate face landmark detection library using PyTorch; Support 68-point semi-frontal and 39-point profile landmark detection; Support both coordinate-based and heatmap-based inference; Up to 100FPS landmark inference on CPU.
A collection of face related papers
Matlab implementation of facial landmark detection by deep multi-task learning
Face related datasets
Face Alignment by Mobilenetv2
[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition
Real time face alignment
Training & Inference Code of PRNet in PyTorch 1.1.0
The MXNet Implementation of Stacked Hourglass and Stacked SAT for Robust 2D and 3D Face Alignment
One-shot Learning and deep face recognition notebooks and workshop materials
A TensorFlow implementation of the Mnemonic Descent Method.
Training code for the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.
实现常用基于深度学习的人脸检测算法
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