Research ArticleOpen AccessGoogle Scholar indexed
Research on Face Recognition Algorithm Based on Robust 2DPCA
Department of Mathematics, College of Science, Shanghai University, Shanghai, China
Department of Mathematics, College of Science, Shanghai University, Shanghai, China
Department of Mathematics, College of Science, Shanghai University, Shanghai, China
- 1 Department of Mathematics, College of Science, Shanghai University, Shanghai, China
- 2 Department of Mathematics, College of Science, Shanghai University, Shanghai, China
- 3 Department of Mathematics, College of Science, Shanghai University, Shanghai, China
Advances in Pure Mathematics·Volume 11 (2021)·Pages 149–161·Published 5 February 2021·DOI10.4236/apm.2021.112010
Copy link · social · email
Abstract
As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA. To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm. Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral. Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.
Keywords2DPCAFace RecognitionDimension ReductionF Norm
- Wold, S., Esbensen, K.H. and Geladi, P. (1987) Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 2, 37-52. https://doi.org/10.1016/0169-7439(87)80084-9
- Yang, J., Zhang, D., Frangi, A.F. and Yang, J.-Y. (2004) Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 131-137. https://doi.org/10.1109/TPAMI.2004.1261097
- Jeong, Y.W. and Kim, H.S. (2009) New Speaker Adaptation Method Using 2-D PCA. IEEE Signal Processing Letters, 17, 193-196. https://doi.org/10.1109/LSP.2009.2036696
- Wang, D. and Lu, H. (2012) Object Tracking via 2DPCA and ℓ 1 -Regularization. IEEE Signal Processing Letters, 19, 711-714. https://doi.org/10.1109/LSP.2012.2215320
- Kong H., Li, X. and Wang, L. (2005) Generalized 2D Principal Component Analysis. 2005 IEEE International Joint Conference on Neural Networks, Montreal, 31 July-4 August 2005, 108-112. https://doi.org/10.1109/IJCNN.2005.1555814
- Zhang, D. and Zhou, Z.H. (2005) (2D) 2 PCA: Two-Directional Two-Dimensional PCA for Efficient Face Representation and Recognition. Neurocomputing, 69, 224-231. https://doi.org/10.1016/j.neucom.2005.06.004
- Xu A., Jin X. and Jiang Y (2006) Complete Two-Dimensional PCA for Face Recognition. 18th International Conference on Pattern Recognition, Hong Kong, 20-24 August 2006, 481-484. https://doi.org/10.1109/ICPR.2006.395
- Kim, Y.G., Song, Y.J., Chang, U.D., Kim, D.-W., Yun, T.-S. and Ahn, J.-H. (2008) Face Recognition Using a Fusion Method Based on Bidirectional 2DPCA. Applied Mathematics and Computation, 205, 601-607. https://doi.org/10.1016/j.amc.2008.05.032
- Kwak, N. (2008) Principal Component Analysis Based on L1-Norm Maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1672-1680. https://doi.org/10.1109/TPAMI.2008.114
- Li, X., Pang, Y. and Yuan, Y. (2010) L1-Norm-Based 2DPCA. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1170-1175. https://doi.org/10.1109/TSMCB.2009.2035629
- Ng, A.Y. (2004) Feature Selection, L1 vs. L2 Regularization, and Rotational Invariance. Proceedings of the 21st International Conference on Machine Learning, Banff, June 2004, 78. https://doi.org/10.1145/1015330.1015435
- Gao, Q., Ma, L. and Liu, Y. (2018) Angle 2DPCA: A New Formulation for 2DPCA. IEEE Transactions on Systems, Man, and Cybernetics, 48, 1672-1678. https://doi.org/10.1109/TCYB.2017.2712740