Authors:
Peter Dunker
1
and
Melanie Keller
2
Affiliations:
1
Fraunhofer Institute for Digital Mediatechnology (IDMT), Germany
;
2
Robert Bosch GmbH, Germany
Keyword(s):
Illumination normalization, face recognition, perception-inspired, retinex, diffusion filter, local operations.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Telecommunications
Abstract:
Face recognition has been actively investigated by the scientific community and has already taken its place in modern consumer software. However, there are still major challenges remaining e.g. preventing negative influence from varying illumination, even with well known face recognition systems. To reduce the performance drop off caused by illumination, normalization methods can be applied as pre-processing step. Well known approaches are linear regression or local operations. In this publication we present the results of a two-step evaluation for real-world applications of a wide range of state-of-the-art illumination normalization algorithms. Further we present a new taxonomy of these methods and depict advanced algorithms motivated by the pre-eminent human abilities of illumination normalization. Additionally we introduce a recent bio-inspired algorithm based on diffusion filters that outperforms most of the known algorithms. Finally we deduce the theoretical potentials and pract
ical applicability of the normalization methods from the evaluation results.
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