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

Iris Recognition in Multiple Spectral Bands: From Visible to Short Wave Infrared

The human iris is traditionally imaged in Near Infrared (NIR) wavelengths (700nm-900nm) for iris recognition. The absorption co-efficient of color inducing pigment in iris, called Melanin, decreases after 700nm thus minimizing its effect when iris is imaged at wavelengths greater than 700nm. This thesis provides an overview and explores the efficacy of iris recognition at different wavelength bands ranging from visible spectrum (450nm-700nm) to NIR (700nm-900nm) and Short Wave Infrared (900nm-1600nm). Different matching methods are investigated at different wavelength bands to facilitate cross-spectral iris recognition.;The iris recognition analysis in visible wavelengths provides a baseline performance when iris is captured using common digital cameras. A novel blob-based matching algorithm is proposed to match RGB (visible spectrum) iris images. This technique generates a match score based on the similarity between blob like structures in the iris images. The matching performance of the blob based matching method is compared against that of classical \u27Iris Code\u27 matching method, SIFT-based matching method and simple correlation matching, and results indicate that the blob-based matching method performs reasonably well. Additional experiments on the datasets show that the iris images can be matched with higher confidence for light colored irides than dark colored irides in the visible spectrum.;As part of the analysis in the NIR spectrum, iris images captured in visible spectrum are matched against those captured in the NIR spectrum. Experimental results on the WVU multispectral dataset show promise in achieving a good recognition performance when the images are captured using the same sensor under the same illumination conditions and at the same resolution. A new proprietary \u27FaceIris\u27 dataset is used to investigate the ability to match iris images from a high resolution face image in visible spectrum against an iris image acquired in NIR spectrum. Matching in \u27FaceIris\u27 dataset presents a scenario where the two images to be matched are obtained by different sensors at different wavelengths, at different ambient illumination and at different resolution. Cross-spectral matching on the \u27FaceIris\u27 dataset presented a challenge to achieve good performance. Also, the effect of the choice of the radial and angular parameters of the normalized iris image on matching performance is presented. The experiments on WVU multispectral dataset resulted in good separation between genuine and impostor score distributions for cross-spectral matching which indicates that iris images in obtained in visible spectrum can be successfully matched against NIR iris images using \u27IrisCode\u27 method.;Iris is also analyzed in the Short Wave Infrared (SWIR) spectrum to study the feasibility of performing iris recognition at these wavelengths. An image acquisition setup was designed to capture the iris at 100nm interval spectral bands ranging from 950nm to 1650nm. Iris images are analyzed at these wavelengths and various observations regarding the brightness, contrast and textural content are discussed. Cross-spectral and intra-spectral matching was carried out on the samples collected from 25 subjects. Experimental results on this small dataset show the possibility of performing iris recognition in 950nm-1350nm wavelength range. Fusion of match scores from intra-spectral matching at different wavelength bands is shown to improve matching performance in the SWIR domain

Copernicus Downstream Service Supports Nature-Based Flood Defense: Use of Sentinel Earth Observation Satellites for Coastal Needs

This is a copy of the article published in Sea-Technology Magazine, which the authors have bought the rights to redistribute.With an uncertain future that includes climate change, sea level rise and increasing coastal populations, being able to make informed policy decisions in coastal zones will be critical for ensuring the well-being of citizens, the environment and the sustainability of economic activities. Earth observation (EO) can be used to efficiently and systematically provide the key information needed to make these decisions. However, getting access to the right EO in- formation can be a complicated and costly business, limiting availability. However, the launch in April 2014 of the first Sentinel satellite from Europe’s flagship EO program, Copernicus, represents a major advance in the availability of EO data, which has great potential to benefit numerous sectors involved in marine and coastal activities. We discuss some examples of applications being developed and give an example of a new service which intends to support nature-based flood defense schemes.The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement n° 607131. All views presented are those of the authors. The EU is not liable for any use that may be made of the information contained herein.PDF, 5 pages, 20.4 M

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

Conjunctival Vasculature (CV) as a unique modality for authentication, using Steady Illumination Colour Local Ternary Pattern (SIcLTP)

it has been proved that a new biometric modality based on the patterns of conjunctival vasculature performs well in visible spectrum. The vessels of the conjunctiva could be seen on the visible part of the sclera; these vessels are very rich and contain unique details in the visible spectrum of light. In this paper we have explored the feature extraction technique for conjunctival vasculature using Steady Illumination colour Local Ternary Patterns(SIcLTP). The concept of LTP as argued in various earlier published papers is that, it is very robust to noise and gives rich information at the pixel level. In this paper before feature extraction the images are converted into YIQ colour space from RGB colour space to do away with the redundant information demonstrated by RGB colour space. Further the image similarity and dissimilarity is found out using zero-mean sum of squared differences between the two equally sized images. The results received with AUC (Area Under ROC Curve) being 0.947, demonstrates the richness of the texture pattern of conjunctival vasculature and robustness of the method being used. It is concluded that this texture pattern is a very promising biometric modality which could be used for identification

Infrared Image Super-Resolution: Systematic Review, and Future Trends

Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at \url{https://github.com/yongsongH/Infrared_Image_SR_SurveyComment: Submitted to IEEE TNNL