RLP Project: A novel method for face recognition and facial expression analysis
Over the past few years, there has been an increased interest in automatic facial expression recognition and face recognition. We presents the RLP-Project, a project for face analysis based on a radial luminance projection.
This project is organized following the two steps: feature extraction and classification. The feature extraction is performed by the ./process_database.m script. This code operates on the selected database and save the features vetors in the ./RLP_resuts directory. The classification step is perform by the function ./SVM/fit_svm.m or the script ./SVM/loop_fit.m for multiple runs. More details in ./SVM/readme.txt.
This project was implemented to perform face recognition and facial expression analysis on several datasets.
- JAFFE Database
- The Yale Face Database B
- The ORL Database of Faces
If you use any of the resources provided on this page in any of your publications we ask you to cite the following work and the work for a relevant submodule you used.
A face analysis method using Radial Luminance Projection Arthur DUPUY. EII 4th year InnovR at INSA Rennes, 2019
Cambridge face tracker (CLM-framework)
Constrained Local Neural Fields for robust facial landmark detection in the wild Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
You have to respect CLM-framework, libSVM and OpenCV licenses.
Furthermore you have to respect the licenses of the datasets used for model training - https://gitlab.insa-rennes.fr/adupuy/RLP_Project/wikis/datasets