Adaptive context-aware filter fusion for face recognition on bad illumination

Authors:
Nam Mi Young;Md. Rezaul Bashar;Phill Kyu Rhee
Affiliations:
Intelligent Technology Laboratory, Dept. of Computer Science & Engineering, Inha University, Incheon, Korea;Intelligent Technology Laboratory, Dept. of Computer Science & Engineering, Inha University, Incheon, Korea;Intelligent Technology Laboratory, Dept. of Computer Science & Engineering, Inha University, Incheon, Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 7
Cited 1

From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing

Digital Image Processing
Illumination Modeling and Normalization for Face Recognition

AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A Bilinear Illumination Model for Robust Face Recognition

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Fuzzy ARTMAP network with evolutionary learning

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Quad phase minimum average correlation energy filters for reduced memory illumination tolerant face authentication

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
"Corefaces"- robust shift invariant PCA based correlation filter for illumination tolerant face recognition

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Adaptive audio-based context recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

At present, the performance of face recognition system depends much on the variations in illumination. To solve this problem, this paper presents an adaptable face recognition approach that uses filter fusion representation. The key idea is to use context-aware filter fusion to get better image from a bad illumination one. Genetic algorithm is the tool for adaptation for individual context category. These can provide robust face recognition on illumination context-awareness under uneven environments. Gabor wavelet representation can also provide a robust feature for image enhancement. Using these approaches, we have developed a robust face recognition technique that can recognize with a notable success and it has been tested on Inha DB and FERET face images.