A novel illumination-robust face recognition using statistical and non-statistical method

Authors:
Bongjin Jun;Jinseok Lee;Daijin Kim
Affiliations:
Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang 790-784, South Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang 790-784, South Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu, Pohang 790-784, South Korea
Venue:
Pattern Recognition Letters
Year:
2011

Citing 12
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions

AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Separating Style and Content with Bilinear Models

Neural Computation
Face Description with Local Binary Patterns: Application to Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Enhanced local texture feature sets for face recognition under difficult lighting conditions

AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
AdaBoost gabor fisher classifier for face recognition

AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

IEEE Transactions on Image Processing
Face recognition by independent component analysis

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.10

Visualization

Abstract

This paper proposes a novel illumination-robust face recognition technique that combines the statistical global illumination transformation and the non-statistical local face representation methods. When a new face image with arbitrary illumination is given, it is transformed into a number of face images exhibiting different illuminations using a statistical bilinear model-based indirect illumination transformation. Each illumination transformed image is then represented by a histogram sequence that concatenates the histograms of the non-statistical multi-resolution uniform local Gabor binary patterns (MULGBP) for all the local regions. This is facilitated by dividing the input image into several regular local regions, converting each local region using several Gabor filters, and converting each Gabor filtered region image into multi-resolution local binary patterns (MULBP). Finally, face recognition is performed by a simple histogram matching process. Experimental results demonstrate that the proposed face recognition method is highly robust to illumination variation as exhibited in the real environment.