Seeing People in the Dark: Face Recognition in Infrared Images
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Face recognition with visible and thermal infrared imagery
Computer Vision and Image Understanding - Special issue on Face recognition
Fusion of Visual and Thermal Signatures with Eyeglass Removal for Robust Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 - Volume 08
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
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Illumination Invariant Face Recognition Using Near-Infrared Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Fusion: Algorithms and Applications
Image Fusion: Algorithms and Applications
PSO versus AdaBoost for feature selection in multimodal biometrics
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
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This paper presents two novel image fusion schemes for combining visible and near infrared face images (NIR), aiming at improving the verification performance. Sub-band decomposition is first performed on the visible and NIR images separately. In both cases, we further employ particle swarm optimization (PSO) to find an optimal strategy for performing fusion of the visible and NIR sub-band coefficients. In the first scheme, PSO is used to calculate the optimum weights of a weighted linear combination of the coefficients. In the second scheme, PSO is used to select an optimal subset of features from visible and near infrared face images. To evaluate and compare the efficacy of the proposed schemes, we have performed extensive verification experiments on the IRVI database. This database was acquired in our laboratory using a new sensor that is capable of acquiring visible and near infrared face images simultaneously thereby avoiding the need for image calibration. The experiments show the strong superiority of our first scheme compared to NIR and score fusion performance, which already showed a good stability to illumination variations.