Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition
New Jersey Institute of Technology · George Mason University
Abstract
This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed…
Citation impact
- FWCI
- 25.60
- Percentile
- 100%
- References
- 41
Authors
2Topics & keywords
- Pattern recognition (psychology)
- Artificial intelligence
- Eigenface
- Gabor wavelet
- Facial recognition system
- Linear discriminant analysis
- Feature vector
- Computer science
- Reduced inequalities