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

High Dimensional Low Rank plus Sparse Matrix Decomposition

This paper is concerned with the problem of low rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems with complexity that scales with the dimension of the data, which limits their scalability. Furthermore, existing randomized approaches mostly rely on uniform random sampling, which is quite inefficient for many real world data matrices that exhibit additional structures (e.g. clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out using a small data sketch formed from sampled columns/rows. Even when the data is sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly O(r\mu), where \mu is the coherency parameter and r the rank of the low rank component. In addition, adaptive sampling algorithms are proposed to address the problem of column/row sampling from structured data. We provide an analysis of the proposed method with adaptive sampling and show that adaptive sampling makes the required number of sampled columns/rows invariant to the distribution of the data. The proposed approach is amenable to online implementation and an online scheme is proposed.Comment: IEEE Transactions on Signal Processin

Randomized Subspace Learning Approach For High Dimensional Low Rank Plus Sparse Matrix Decomposition

In this paper, a randomized algorithm for high dimensional low rank plus sparse matrix decomposition is proposed. Existing decomposition methods are not scalable to big data since they rely on using the whole data to extract the low-rank/sparse components, and are based on an optimization problem whose dimensionality is equal to the dimension of the given data. We reformulate the low rank plus sparse matrix decomposition problem as a column-row subspace learning problem. It is shown that when the column/row subspace of the low rank matrix is incoherent with the standard basis, the column/row subspace can be obtained from a small random subset of the columns/rows of the given data matrix. Thus, the high dimensional matrix decomposition problem is converted to a subspace learning problem, which is a low-dimensional optimization problem, and the proposed method uses a small random subset of the data rather than the whole big data matrix. In the provided analysis, it is shown that the sufficient number of randomly sampled columns/rows scales linearly with the rank and the coherency parameter of the low rank component