Multi-Criteria in Discriminant Analysis to Find the Dominant Features
Abstract: A crucial problem in
biometrics is enormous dimensionality. It will have an impact on the costs involved.
Therefore, the feature extraction plays a significant role in biometrics
computational. In thisresearch, a novel approach to extract the features is
proposed for facial image recognition. Four criteria of the Discriminant
Analysis have been modeled to find the dominant features. For each criterion is
an objective function, it was derived to obtain the optimum values. The optimum
values can be solved by using generalized the Eigenvalue problem associated to
the largest Eigenvalue. The modeling results were employed to recognize the
facial image by the multi-criteria projection to the original data. The
training sets were also processed by using the Eigenface projection to avoid
the singularity problem cases. The similarity measurements were performed by
using four different methods, i.e. Euclidian Distance,Manhattan, Chebyshev, and
Canberra. Feature extraction and analysis results using multi-criteria have shown
better results than the other appearance method, i.e. Eigenface (PCA),
Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality
Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal
Locality Preserving Projection or O-LPP).
Keywords: multi-criteria,
discriminant analysis, features extraction, singularity problem, facial
recognition
Author: Arif Muntasa, Indah
Agustien Siradjuddin, Rima Tri Wahyuningrum
Journal Code: jptkomputergg160310
