Combining classifiers for enhanced face recognition

@inproceedings{Nebti2015CombiningCF,
  title={Combining classifiers for enhanced face recognition},
  author={Salima Nebti and BOUKEMARA FADILA},
  year={2015}
}
This work is devoted to improve face recognition accuracy through a combination of two known effective classifiers namely Support Vector Machines (SVM) and Hidden Markov Models (HMM). The SVM is fed with PCA features and the used HMM is a one dimensional seven state model whose features are based on singular value decomposition (SVD). We used the majority vote combination rule to fuse the outputs of the considered SVM and HMM; using this representation a high recognition rate of 100% has been… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 38 references

Review on Local Binary Pattern For Face Recognition

R. Garg, I. S. Rajput
International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014), ISSN : 2347 – 8446, Vol. 2, Issue 2, Ver. 2 • 2014
View 5 Excerpts
Highly Influenced

Eigenfaces for Recognition

Journal of Cognitive Neuroscience • 1991
View 5 Excerpts
Highly Influenced

A New Approach for Face Image Enhancement and Recognition, International Journal of Advanced Science and Technology

N.Amani, A. Shahbahrami, M. Nahvi
2013
View 1 Excerpt

A

S. Suhas
Kurhe, Dr.P. Khanale, “ Face Recognition Using Principal Component Analysis and Linear Discriminant Analysis on Holistic Approach in Facial Images Database”, IOSR Journal of Engineering e-ISSN:2250- 3021, ISSN: 2278-8719, Vol. 2, Issue 12 • 2012
View 3 Excerpts