Fingerprint Compression Based on Sparse Representation

Abstract

Biometric identification systems are in use for last many years for the purpose of personal identification, uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth dealing with such enormous amount of information can often present difficulties. As per my literature survey, there is no such method that uses compressive sensing and adaptive learning dictionary to compress image along with neural network to estimate the results. In the given algorithm, a dictionary of predefined fingerprint patches is constructed which is than quantized and encoded.

3-D Adaptive Sparsity Based Image Compression With Applications to Optical Coherence Tomography

“…Given a 2D image of size N × M , most sparse representation based compression methods [29–32] preprocess the input image by breaking it into ϒ non-overlapping patches X i ∈ ℝ n × m , i = 1,2,..., ϒ, n < N and m < M . Here, i indexes a particular patch with respect to the lateral and axial position of its center in a 2D image.…”

“…To address the first problem, machine learning algorithms such as the K-SVD [35] and recursive least squares dictionary learning algorithm (RLS-DLA) [31] are widely utilized to design the dictionary D from a large number of training images with relevant content [29–32]. To address the second problem, which is nondeterministic polynomial-time hard (NP-hard) [36], previous methods often utilize the orthogonal matching pursuit (OMP) algorithm [37] to obtain an approximate solution [29–32].…”

“…To address the second problem, which is nondeterministic polynomial-time hard (NP-hard) [36], previous methods often utilize the orthogonal matching pursuit (OMP) algorithm [37] to obtain an approximate solution [29–32]. The OMP algorithm aims to select one atom that can best describe x i per iteration based on the correlations between the atoms in D and the projection residual r i , where r i = x i − Dα i .…”

“…retina), which can create a 3D volume (of size N × M × Z ). To compress the 3D OCT volume, one simple way is to adopt 2D based sparse compression methods [29–32] to handle each slice (B-scan) independently. However, the 2D scheme cannot exploit the high correlations among nearby slices.…”

“…Alternatively, inspired by the sparse coding mechanism of the mammalian vision system [19], sparse representation with a learned dictionary has been demonstrated to be a very powerful tool for many image processing and computer vision applications [20–28]. In [29–32], sparse representation has been utilized for compressing single-band 2D images, outperforming DCT/DWT techniques. These methods first decompose the input signal as a linear combination of a few atoms from an over-complete dictionary consisting of basis functions.…”