FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint identification using space invariant transforms
Pattern Recognition Letters
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Fingerprint Matching Using an Orientation-Based Minutia Descriptor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Fingerprint Orientation Field Registration Algorithms
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Fingerprint verification by fusion of optical and capacitive sensors
Pattern Recognition Letters
Fingerprint minutiae matching using the adjacent feature vector
Pattern Recognition Letters
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Fingerprint matching combining the global orientation field with minutia
Pattern Recognition Letters
Fingerprint matching based on global alignment of multiple reference minutiae
Pattern Recognition
Score normalization in multimodal biometric systems
Pattern Recognition
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
Directional filter bank-based fingerprint feature extraction and matching
IEEE Transactions on Circuits and Systems for Video Technology
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Most of the proposed methods used for fingerprint verification are based on local visible features called minutiae. However, due to problems for extracting minutiae from low-quality fingerprint images, other discriminatory information has been considered. In this paper, the idea of decision-level fusion of orientation, texture, and spectral features of fingerprint image is proposed. At first, a value is assigned to the similarity of block orientation field of two-fingerprint images. This is also performed for texture and spectral features. Each one of the proposed similarity measure does not need core-point existence and detection. Rotation and translation of two fingerprint images are also taken into account in each method and all points of fingerprint image are employed in feature extraction. Then, the similarity of each feature is normalized and used for decision-level fusion of fingerprint information. The experimental results on FVC2000 database demonstrate the effectiveness of the proposed fusion method and its significant accuracy.