IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 'No Panacea Theorem' for Multiple Classifier Combination
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Robust speaker verification via fusion of speech and lip modalities
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Score normalization in multimodal biometric systems
Pattern Recognition
Multimodal authentication using asynchronous HMMs
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
IEEE Transactions on Multimedia
Multimodal speaker identification using an adaptive classifier cascade based on modality reliability
IEEE Transactions on Multimedia
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
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A new method is proposed to estimate the optimal weighting parameter for combining audio (speech) and visual (face) information in person identification, based on estimating probability density functions (pdfs) for classifier scores under Gaussian assumptions. Performance comparisons with real and simulated data indicate that this method has advantages in reducing bias and variance of the estimation relative to other methods tried, so achieving a robust estimator of the optimal weighting parameter. Another contribution is that we propose the bootstrap method to compare performances of different algorithms for estimating the optimal weighting parameter, so providing a strict criterion in comparing algorithms of this kind. Using simulated data, for which the pdf is controlled and known, we show that the advantages of the method hold up when the underlying Gaussian assumption is violated. The main drawback is that we have to choose an adjustable parameter, and it is not clear how this should best be done.