A Compared Study Between Some Subspace Based Algorithms
This is an incremental study comparing subspace-based algorithms for face recognition, with potential relevance to researchers in computer vision.
The paper tackled face recognition by proposing ECA (2DECA) based on Renyi entropy contribution from PCA (2DPCA) and studying 2DL1-PCA and 2DR1-PCA, comparing their recognition accuracy and operational efficiency through experiments.
The technology of face recognition has made some progress in recent years. After studying the PCA, 2DPCA, R1-PCA, L1-PCA, KPCA and KECA algorithms, in this paper ECA (2DECA) is proposed by extracting features in PCA (2DPCA) based on Renyi entropy contribution. And then we conduct a study on the 2DL1-PCA and 2DR1-PCA algorithms. On the basis of the experiments, this paper compares the difference of the recognition accuracy and operational efficiency between the above algorithms.