Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
A Comparative Analysis of Face Recognition Performance with Visible and Thermal Infrared Imagery
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Modal 2D and 3D Biometrics for Face Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
Three-Dimensional Face Recognition
International Journal of Computer Vision
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Illumination Invariant Face Recognition Using Near-Infrared Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Quadratic Discriminant Analysis
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
IR and visible light face recognition
Computer Vision and Image Understanding
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
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Existing literature compares various biometric modalities of the face for human identification. The common criterion used for comparison is the recognition rate of different face modalities using the same recognition algorithms. Such comparisons are not completely unbiased as the same recognition algorithm or features may not be suitable for every modality of the face. Moreover, an important aspect which is overlooked in these comparisons is the amount of variation present in each modality which will ultimately effect the database size each modality can handle. This paper presents such a comparison between the most common biometric modalities of the face namely visible, thermal infra-red and range images. Experiments are performed on the Equinox and the FRGC databases with results indicating that visible images capture more interpersonal variations of the human face compared to thermal IR and range images. We conclude that under controlled conditions, visible face images have a greater potential of accommodating large databases compared to long-wave IR and range images.