Probability and statistics
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
Comparison of visible and infra-red imagery for face recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with visible and thermal infrared imagery
Computer Vision and Image Understanding - Special issue on Face recognition
Face Recognition in Hyperspectral Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Face recognition in hyperspectral images
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Recognize color face images using complex eigenfaces
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
| Hi-index | 0.00 |
Face recognition based on spatial features has been widely used for personal identity verification for security-related applications. Recently, near-infrared spectral reflectance properties of local facial regions have been shown to be sufficient discriminants for accurate face recognition. In this paper, we compare the performance of the spectral method with face recognition using the eigenface method on single-band images extracted from the same hyperspectral image set. We also consider methods that use multiple original and PCA-transformed bands. Lastly, an innovative spectral eigenfacemethod which uses both spatial and spectral features is proposed to improve the quality of the spectral features and to reduce the expense of the computation. The algorithms are compared using a consistent framework.