Total views : 210

The Performance of LTS-based Regression Methods in Face Recognition with Occluded Images

Affiliations

  • Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

Abstract


In this paper, we compared the performance of several Least Trimmed Squares (LTS) based methods for face image recognition. The focus was on the problem of severely occluded face images. The performance of random LTS, Fast LTS and GA-LTS (which is based on genetic algorithm) to a benchmark dataset with occluded query images was examined. The best method was the one being least affected by the occluded images and produces highest recognition rates. The AT&T and Yale Data were used to assess the methods in performing face recognition. The query images were contaminated with salt and pepper noise and the recognition rates were measured when the contaminated images were used as query image in the context of linear regression. Results show that the random LTS outperforms the rest in dealing with occluded images with higest recognition rates.

Keywords

Face Recognition, Least Trimmed Squares with Genetic Algorithm, Occluded Images.

Full Text:

 |  (PDF views: 157)

References


  • Etemad K, Chellappa R. Discriminant analysis for recognition of human face images. JOSA A. 1997; 14(8):1724–33.
  • Fidler S, Skocaj D Leonardis A. Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006; 28(3):337–50.
  • Phillips PJ, et al. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000; 22(10):1090–104.
  • Lai J, Jiang X. Robust face recognition using trimmed linear regression. ICASSP; 2013.
  • Rousseeuw PJ, Leroy AM. Robust regression and outlier detection; 2003. p. 360.
  • Rousseeuw PJC. Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association. 1990; 85(411):633–9.
  • Toh KKV, Isa NAM. Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Processing Letters. 2010; 17(3):281–4.
  • Wagner A, et al. Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012; 34(2):372–86.
  • Jia H, Martinez AM. Face recognition with occlusions in the training and testing sets. 8th IEEE International Conference on Automatic Face and Gesture Recognition FG’08; 2008.
  • Jiang X. Linear subspace learning-based dimensionality reduction. IEEE Signal Processing Magazine. 2011; 28(2):16–26.
  • Naseem I, Togneri R, Bennamoun M. Linear regression for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010; 32(11):2106–12.
  • Rousseeuw PJ. Least median of squares regression. Journal of the American Statistical Association. 1984; 79(388):871–80.
  • Bai E-W. A random least-trimmed-squares identification algorithm. Automatica. 2003; 39(9):1651–9.
  • Marazzi A. Algorithms, Routines, and S-Functions for Robust Statistics. Taylor and Francis; 1993.
  • Rousseeuw PJ, Van Driessen K. Computing LTS regression for large data sets. Institute of Mathematical Statistics Bulletin, Citeseer; 1999.
  • Rousseeuw PJ, Van Driessen K. Computing LTS regression for large data sets. Data Mining and Knowledge Discovery. 2006; 12(1):29–45.
  • Satman MH. A genetic algorithm based modification on the LTS Algorithm for large data sets. Communications in Statistics - Simulation and Computation. 2011; 41(5):644–52.
  • Cai D, et al. Learning a spatially smooth subspace for face recognition. 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’07); 2007.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.