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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Computer Graphics and Applications
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
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Using Hidden Markov Models and Wavelets for Face Recognition
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Face recognition: A literature survey
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Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural hidden Markov models: An application to handwritten numeral recognition
Intelligent Data Analysis
Score normalization in multimodal biometric systems
Pattern Recognition
A separable low complexity 2D HMM with application to face recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Artificial Neural Network Based Automatic Face Model Generation System from Only One Fingerprint
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
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Pattern Recognition
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A feature extraction method for use with bimodal biometrics
Pattern Recognition
Hybrid intelligent techniques for MRI brain images classification
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Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
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Expert Systems with Applications: An International Journal
Fusion of biometric systems using Boolean combination: an application to iris-based authentication
International Journal of Biometrics
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The goal of this paper is threefold: (i) propose a novel face and fingerprint feature modeling using the structural hidden Markov models (SHMMs) paradigm, (ii) explore the use of some feature extraction techniques such as ridgelet transform, discrete wavelet transform with various classifiers for biometric identification, and (iii) determine the best method for classifier combination. The experimental results reported in both fingerprint and face recognition reveal that the SHMMs concept is promising since it has outperformed several state-of-the-arts classifiers when combined with the discrete wavelet transform. Besides, this study has shown that the ridgelet transform without principal components analysis (PCA) dimension reduction fits better with the support vector machines (SVMs) classifier than it does with the SHMMs in the fingerprint recognition task. Finally, these results also reveal a small improvement of the bimodal biometric system over unimodal systems; which suggest that a most effective fusion scheme is necessary.