Open AccessApplication of MEEMD in post‐processing of dimensionality reduction methods for face recognition
Author(s) -
Abbad Abdelghafour,
Elharrouss Omar,
Abbad Khalid,
Tairi Hamid
Publication year - 2019
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2018.5033
Subject(s) - computer science , dimensionality reduction , discriminative model , pattern recognition (psychology) , artificial intelligence , facial recognition system , curse of dimensionality , outlier , face (sociological concept) , reduction (mathematics) , data mining , machine learning , mathematics , social science , geometry , sociology
Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to pre‐processing approaches. However, they also require post‐processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post‐processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.