Self-adaptive classifier fusion for expression-insensitive face recognition

Authors:
Eun Sung Jung;Soon Woong Lee;Phill Kyu Rhee
Affiliations:
Dept. Of Computer Science & Engineering, Inha University, Yong-Hyun Dong, Incheon, South Korea;Dept. Of Computer Science & Engineering, Inha University, Yong-Hyun Dong, Incheon, South Korea;Dept. Of Computer Science & Engineering, Inha University, Yong-Hyun Dong, Incheon, South Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 8
Cited 0

Reconfigurable Context-Sensitive Middleware for Pervasive Computing

IEEE Pervasive Computing
Illumination and Expression Invariant Face Recognition with One Sample Image

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition in the Presence of Expression and/or Illumination Variation

AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Expression-invariant 3D face recognition

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Recognizing expression variant faces from a single sample image per class

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Designing classifier fusion systems by genetic algorithms

IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We address a self-adaptive face recognition scheme which is insensitive to facial expression variations. The proposed method takes advantage of self-adaptive classifier fusion based on facial geometry and RBF warping technology. Most previous face recognition schemes usually show vulnerability under changing facial expressions. The proposed scheme discriminates input face images into one of several context categories. The context categories are decided by unsupervised learning method based on the facial geometries that are derived from either scanned mosaic face images and/or coordinates of facial feature points. The proposed method provides a self-adaptive preprocessing and feature representation in accordance with the identified context category using the genetic algorithm and knowledge accumulation mechanism. The superiority of the proposed method is shown using FERET database where face images are relatively exposed to wide range of facial expression variation.