3,171 research outputs found
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
Noise Tolerance under Risk Minimization
In this paper we explore noise tolerant learning of classifiers. We formulate
the problem as follows. We assume that there is an
training set which is noise-free. The actual training set given to the learning
algorithm is obtained from this ideal data set by corrupting the class label of
each example. The probability that the class label of an example is corrupted
is a function of the feature vector of the example. This would account for most
kinds of noisy data one encounters in practice. We say that a learning method
is noise tolerant if the classifiers learnt with the ideal noise-free data and
with noisy data, both have the same classification accuracy on the noise-free
data. In this paper we analyze the noise tolerance properties of risk
minimization (under different loss functions), which is a generic method for
learning classifiers. We show that risk minimization under 0-1 loss function
has impressive noise tolerance properties and that under squared error loss is
tolerant only to uniform noise; risk minimization under other loss functions is
not noise tolerant. We conclude the paper with some discussion on implications
of these theoretical results
Sparse multinomial kernel discriminant analysis (sMKDA)
Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets
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