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.
Details of the implementations can be found in
Bor-Chun Chen, Chu-Song Chen, Winston H. Hsu. Review and Implementation of High-Dimensional Local Binary Patterns and Its Application to Face Recognition, Technical Report TR-IIS-14-003, Institute of Information Science, Academia Sinica, 2014. (PDF)
If you use this code for your research, please kindly cite the technical report above.
For more information, pelase visit the project website
##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 bcsiriuschen@gmail.com