etos-landmark

a high accuracy facial landmark detection model.

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Created by Wayne Wu.

boundary-aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin.

Prerequisites

• Linux
• Python3.6 is tested
• NVIDIA GPU + CUDA CuDNN is tested

Getting Started

Installing

0. Install prerequisites for Caffe (http://caffe.berkeleyvision.org/installation.html#prequequisites, cuda may be needed)
1. git clone https://github.com/etosworld/etos-landmark
2. make all
3. ./compile.sh
4. run ./caffe_LAB to see one simple image result

Wider Facial Landmark in the Wild (WFLW) Dataset Download

Wider Facial Landmarks in-the-wild (WFLW) is a new proposed face dataset. It contains 10000 faces (7500 for training and 2500 for testing) with 98 fully manual annotated landmarks.

0. WFLW Training and Testing images [Google Drive] [Baidu Drive]
1. WFLW Face Annotations
2. Unzip above two packages and put them on './datasets/WFLW/'

Simply run this script to download annotations of WFLW

#! ./scripts/download/download_wflw_annotation.sh
bash ./scripts/download/download_wflw_annotation.sh WFLW

Refer

LAB