Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Identity Verification Based on Infrared Face Images
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Thermal face recognition in an operational scenario
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Adaptive weighted fusion of local kernel classifiers for effective pattern classification
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
A brief survey on multispectral face recognition and multimodal score fusion
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features
IEEE Transactions on Neural Networks
PSO Based Framework for Weighted Feature Level Fusion of Face and Palmprint
IIH-MSP '12 Proceedings of the 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
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This work studies the intra-bimodal face-based biometric fusion approach composed of the thermal and spatial domains. The distinctive feature of this work is the use of a single camera with two sensors which returns a unique image with thermal and visual images at a time, as opposed to the-state-of-the-art, for example the multibiometric modalities and hyperspectral images. The proposed system represents a practical bimodal approach for real applications. It is composed by a verification architecture based on the Scale-Invariant Feature Transform algorithm (SIFT) with a vocabulary tree, providing a scheme that scales efficiently to a large number of features. The image database consists of front-view thermal and visual image as a single image, which contain facial temperature distributions of 41 different individuals in 2-dimensional format and 18 images per subject, acquired on three different-day sessions. Results showed that visible images gives better accuracy than thermal information, and with independency of range, head images give the most discriminative information. Besides, fusion approaches reached better accuracy, up to 99.45% for score fusion and 100% for decision fusion. This shows the independency of information between visual and thermal images and the robustness of bimodal interaction.