Predicting knowledge in an ontology stream
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and classification/ matching is conventionally adopted, where each stage transforms its input relatively independently. In practice, the interaction between these modules is limited. Some of the errors in this end-to-end sequential processing can be eliminated, especially for the feature extraction stage, by revisiting the input pattern. We propose a feedforward of the original grayscale image data to a feature (minutiae) verification stage in the context of a minutiae-based fingerprint verification system. This minutiae verification stage is based on reexamining the grayscale profile in a detected minutia's spatial neighborhood in the sensed image. We also show that a feature refinement (minutiae classification) stage that assigns one of two class labels to each detected minutia (ridge ending and ridge bifurcation) can improve the matching accuracy by ∼1% and when combined with the proposed minutiae verification stage, the matching accuracy can be improved by ∼3.2% on our fingerprint database. © 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Shai Fine, Yishay Mansour
Machine Learning
Joseph Y. Halpern
aaai 1996
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021