An Efficient Reconfigurable Architecture for Fingerprint Recognition

The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters

Discrimintive Image Warping with Attribute Flow

We address the problem of finding deformation between two images for the purpose of recognizing objects. The challenge is that discriminative features are often transformation-variant (e.g. histogram of oriented gradients, texture), while transformation-invariant features (e.g. intensity, color) are often not discriminative. We introduce the concept of attribute flow which explicitly models how image attributes vary with its deformation. We develop a non-parametric method to approximate this using histogram matching, which can be solved efficiently using linear programming. Our method produces dense correspondence between images, and utilizes discriminative, transformation-variant features for simultaneous detection and alignment. Experiments on ETHZ shape categories dataset show that we can accurately recognize highly deformable objects with few training examples

Person re-identification using deep foreground appearance modeling

Person reidentification is the process of matching individuals from images taken of them at different times and often with different cameras. To perform matching, most methods extract features from the entire image; however, this gives no consideration to the spatial context of the information present in the image. We propose using a convolutional neural network approach based on ResNet-50 to predict the foreground of an image: the parts with the head, torso, and limbs of a person. With this information, we use the LOMO and salient color name feature descriptors to extract features primarily from the foreground areas. In addition, we use a distance metric learning technique (XQDA), to calculate optimally weighted distances between the relevant features. We evaluate on the VIPeR, QMUL GRID, and CUHK03 data sets and compare our results against a linear foreground estimation method, and show competitive or better overall matching performance

Depth Recovery of Complex Surfaces from Texture-less Pairs of Stereo Images

In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT indexes are defined by means of disparity between the matching invariant features in rectified stereo images. The integration process is based on correcting the 3D visible surfaces obtained from SfS using these SIFT indexes. The SIFT indexes based improvement of depth values which are obtained from generalized Lambertian reflectance model is performed by a feed-forward neural network. The experiments are performed to demonstrate the usability and accuracy of the proposed framework

Feature detection using spikes: the greedy approach

A goal of low-level neural processes is to build an efficient code extracting the relevant information from the sensory input. It is believed that this is implemented in cortical areas by elementary inferential computations dynamically extracting the most likely parameters corresponding to the sensory signal. We explore here a neuro-mimetic feed-forward model of the primary visual area (VI) solving this problem in the case where the signal may be described by a robust linear generative model. This model uses an over-complete dictionary of primitives which provides a distributed probabilistic representation of input features. Relying on an efficiency criterion, we derive an algorithm as an approximate solution which uses incremental greedy inference processes. This algorithm is similar to 'Matching Pursuit' and mimics the parallel architecture of neural computations. We propose here a simple implementation using a network of spiking integrate-and-fire neurons which communicate using lateral interactions. Numerical simulations show that this Sparse Spike Coding strategy provides an efficient model for representing visual data from a set of natural images. Even though it is simplistic, this transformation of spatial data into a spatio-temporal pattern of binary events provides an accurate description of some complex neural patterns observed in the spiking activity of biological neural networks.Comment: This work links Matching Pursuit with bayesian inference by providing the underlying hypotheses (linear model, uniform prior, gaussian noise model). A parallel with the parallel and event-based nature of neural computations is explored and we show application to modelling Primary Visual Cortex / image processsing. http://incm.cnrs-mrs.fr/perrinet/dynn/LaurentPerrinet/Publications/Perrinet04tau

A New Approach for Stereo Matching Algorithm with Dynamic Programming

Stereo matching algorithms are one of heavily researched topic in binocular stereo vision. Massive 3D information can be obtained by finding correct correspondence of different points between images captured from different views. Development of stereo matching algorithm is done for obtaining disparity maps i.e. depth information. When disparities computed for scan lines then dense reconstruction becomes time consuming for vision navigation systems. So for pair of stereo images proposed method extracts features points those are at contours in images and then a dynamic program is used to find the corresponding points from each image and calculates disparities. Also to reduce the noise which may lead to incorrect results in stereo correspondence, a new stereo matching algorithm based on the dynamic programming is proposed. Generally dynamic programming finds the global minimum for independent scan lines in polynomial time. While efficient, its performance is far from desired one because vertical consistency between scan lines is not enforced. This method review the use of dynamic programming for stereo correspondence by applying it to a contour instead to individual scan lines. Proposed methodology will obtain the global minimum for contour array in linear time using Longest Common Subsequent (LCS) dynamic programming method with no disparity space image (DSI). DOI: 10.17762/ijritcc2321-8169.15025