This repository contains the implementation of Robust Sparse Smooth Principal Component Analysis (RSSPCA) for face reconstruction and recognition tasks.
RSSPCA is a novel dimensionality reduction method that combines robustness, sparsity and smoothness properties. The algorithm finds the first projection vector by solving the following optimization problem:
where:
-
$c_1$ and$c_2$ are positive constants -
$L$ is a Laplacian matrix representing the two-dimensional spatial structure information of images
- Robust to outliers and noise in face images
- Sparse projection vectors for efficient feature extraction
- Incorporates spatial smoothness constraints
- Suitable for both face reconstruction and recognition tasks
The algorithm was extensively evaluated on six benchmark face databases:
- AR
- FEI
- FERET
- GT
- ORL
- Yale
More benchmark face databases can be found here.
RSSPCA was compared with several state-of-the-art algorithms:
- PCA (Principal Component Analysis)
- PCA-L1
- RSPCA
- RSMPCA
- Clone this repository:
git clone https://github.com/yuzhounh/RSSPCA.git
- Run the demo script in MATLAB:
main.m
For large-scale applications, a parallel computing version is available here.
Jing Wang
- Email: wangjing@xynu.edu.cn
Copyright (C) 2023 Jing Wang. This code is released under the BSD license.
Last updated: January 29, 2024