Authors:
Abdenour Hacine-Gharbi
1
and
Philippe Ravier
2
Affiliations:
1
Bordj Bou Arreridj University, Algeria
;
2
University of Orléans, France
Keyword(s):
Face Recognition, Local Methods, Holistic Methods, Template Matching, Similarity, Mutual Information, Histogram Approach, Bin Number Selection.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Density Estimation
;
Image Understanding
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Similarity and Distance Learning
;
Telecommunications
;
Theory and Methods
Abstract:
In this paper, we investigate the binning problem of joint histogram estimation applied to mutual information based face recognition application. Classical approaches for histograms estimation tend to empirically fix the bin numbers. We evaluate in this work some state of the art rules for automatically choosing the bin numbers. The face recognition problem has been studied in the case of local and holistic methods. The choice’s performance has been evaluated using AT&T database with single sample in the training set. The results show that better accuracy recognition rates can be achieved with data driven bin number choices rather than fixed bin numbers. In the local method, the results show a higher robustness of the automatic vs fixed bin number choice when the regions become smaller.