Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Separating Style and Content with Bilinear Models
Neural Computation
Selection of the optimal parameter value for the Isomap algorithm
Pattern Recognition Letters
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face recognition technique using symbolic PCA method
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Face recognition using Elasticfaces
Pattern Recognition
| Hi-index | 0.01 |
In this paper, we propose a novel face model, called intrinsic face model. Under this model, each face image is divided into three components, i.e., facial commonness difference, individuality difference and intrapersonal difference, to characterize some certain differences conveyed by this image. Then, a new supervised dimensionality reduction technique coined Intrinsic Discriminant Analysis (IDA) is developed. Intrinsic Discriminant Analysis tries to best classify different face images by maximizing the individuality difference, while minimizing the intrapersonal difference. By using perturbation technique to tackle the singularity problem of IDA which occurs frequently in face recognition, we obtain a new appearance-based face recognition method called Intrinsicfaces. A series of experiments to compare our proposed approach with other dimensionality reduction methods are tested on three well-known face databases. Experimental results demonstrate the efficacy of the proposed Intrinsicfaces approach in face recognition.