My implementation of high dimensional lbp feature for face recognition based on
Dong Chen, Xudong Cao, Fang Wen, Jian Sun. Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification. Computer Vision and Pattern Recognition (CVPR), 2013.
I use openCV for face detection and IntraFace for facial landmark detection.
##Prerequisites
###openCV
Install openCV and change the first line in src/Makefile to opencv home directory:
OPENCV_HOME = /path/to/opencv/
###IntraFace
Download IntraFace Library from http://www.humansensing.cs.cmu.edu/intraface/ (I used v1.0)
and put
- libintraface.a to lib/
- DetectionModel-v1.5.yml,TrackingModel-v1.10.yml to data/
- **FaceAlignment.h **, Marcos.h, XXDescriptor.h to include/
##Build
change to src directory and type make
##Usage
If everythings goes right, there will be to binary files in bin/
face-detection will detect the largest face in the input images and crop the faces into a new image.
Usage: face-detection [-m model_file -o output_dir -s output_scale -l min_size] input_images
model_file: face detection model file, default: ../data/fdetector_model.dat
output_dir: output directory for face images, default: ./
output_scale: output face image size, default: 250
min_size: minimal face size for detection, default: 100
input_images: images for face detection
After face detection, we can extract the high dimensional LBP features using extract-lbp:
Usage: extract-lbp [-m model_dir -o output_dir] input_images
model_dir: model directory for landmark detection, default: ../data/
output_dir: output directory for lbp features, default: ./
input_images: face images for featrue extraction
The output will be image_name.lbp which contains 75,520 dimensional lbp features
##Contact
If you have any questions, feel free to contact me at [email protected]