Face_Alignment

This repository is part of my recent project, including implementation of two face alighment paper:

1.Cao X, Wei Y, Wen F, et al. Face alignment by explicit shape regression[J]. International Journal of Computer Vision, 2014, 107(2): 177-190.

2.Ren S, Cao X, Wei Y, et al. Face alignment at 3000 fps via regressing local binary features[C]//Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on. IEEE, 2014: 1685-1692.

Introduction

Explicit Shape Regression(ESR)

• Cascade regressor(boosting)
• Random Fern

Local Binary Features(LBF)

• Cascade feature extractor
• Random forest(Bootstrap)
• Global regression

Implementation

1. ESR is easy to be vectorized. Quick matlab implementation in the above folder. The training/testing data should be cropped into same size
2. LBF is implemented in C++ with a prototype in matlab. Random Forest training and testng are parealleled by Openmp. Overall landmark detection speed is 300fps(740 trees in each stage, tree depth is 5).

Result

Face alignment on images

Real time face alignment on video

Evaluation

The average point-to-point Euclidean error normalized by the inter-ocular distance (measured as the Euclidean distance between the outer corners of the eyes) will be used as the error measure. Each method is trained on a subset of LFW dataset(one thousand images with 74 landmarks) The face detection in ESR is pre-computed using Faceplusplus public API, so ESR seems to have a better performance. Opencv's Haarcascade face detector used in LBF has a high false-positive detection rate.

Explicit Shape Regression(ESR)

Local Binary Features(LBF)

Both regression methods yield good accuracy on most images, however, they suffer large error on face contour(See #landmark1-15 in the right figures)