An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images

Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks

This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low bonafide classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community

Classification of hyperspectral imagery with neural networks: comparison to conventional tools

Efficient exploitation of hyperspectral imagery is of great importance in remote sensing. Artificial intelligence approaches have been receiving favorable reviews for classification of hyperspectral data because the complexity of such data challenges the limitations of many conventional methods. Artificial neural networks (ANNs) were shown to outperform traditional classifiers in many situations. However, studies that use the full spectral dimensionality of hyperspectral images to classify a large number of surface covers are scarce if non-existent. We advocate the need for methods that can handle the full dimensionality and a large number of classes to retain the discovery potential and the ability to discriminate classes with subtle spectral differences. We demonstrate that such a method exists in the family of ANNs. We compare the maximum likelihood, Mahalonobis distance, minimum distance, spectral angle mapper, and a hybrid ANN classifier for real hyperspectral AVIRIS data, using the full spectral resolution to map 23 cover types and using a small training set. Rigorous evaluation of the classification accuracies shows that the ANN outperforms the other methods and achieves ?90% accuracy on test data

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Hyperspectral colour imaging and spectrophotometric instrumentation

The trichromatic nature of commercial photography is strictly connected with the nature of human colour vision, although the characteristics of usual colour imaging devices are quite different from the human visual system. The increase in the number of colour channels for spectral (either multispectral or hyperspectral) imaging is an active field of research with many potential applications in different fields. Each element of the captured scene is specified in the spectral image by the spectral reflectance factor. This measurement is independent of the particular illumination of the scene and allows the colorimetric computation in a device-independent colour space for any chosen illuminant and any observer. This thesis describes the project and construction of a compact spectrophotometric camera, which can be used in both portable and in-situ applications. The compactness is made possible by a suitable image spectral scanning based on an Induced Transmission Filter (ITF). This filter is made by a set of thin-film coatings of dielectric materials with high and low refraction index, whose shape like a wedge induces a wavelength selective transmittance, continuously variable along one direction and uniform in the perpendicular direction. Such a filter, classified as Linearly Variable Filter (LVF), operates continuously from 430nm to 940nm and allows hyperspectral imaging. In traditional scanners the whole apparatus is moved along a path as long as the scene, whereas in this instrument the camera body is still and the LVF is the only moving part. The sequence of operations for wavelength and radiometric calibrations are discussed. The expected acquisition times and number of images as a function of the spectral sampling step are considered. The resulting properties make the instrument easy to use and with short acquisition times. Moreover, overviews of the historic evolution of colour vision fundamentals, colour spaces and spectral imaging technology are given for introducing the reader to the essential concepts useful for the understanding of the text

Fragmented Landscapes: An Archaeology of Transformations in The Pra River Basin, Southern Ghana

This doctoral archaeological research examines the Pra River Basin in southern Ghana through lenses of landscape, temporality, and transformation. Drawing on the Annales school and the writings of Tim Ingold, this study moves away from binary constructions of natural and cultural landscape features toward a more integrated view of the landscape\u27s long human history. The primary temporal focus of this research is the past three millennia but evidence recovered of even more ancient eras is also examined. The artifacts and features documented while surveying this landscape allow us to glimpse pre-Atlantic (pre-1450 CE) settlement patterns, subsistence, and technology, as well as more recent and ongoing transformations of the landscape. Artifacts including ceramics, quartz flakes, stone beads, ground stone tools, and iron slag were found on hilltop sites throughout the surveyed areas. Most of these sites represent a pre-Atlantic pattern of settlement that continues, to a lesser extent, into the early Atlantic era (1450-1700 CE). Long grinding slicks, possibly related to Nyame Akuma production, are present on numerous rock outcrops in the region. Test excavation at an iron smelting site near Adiembra (AD31) yielded a temporally extensive range of dates. The bulk of the slag was deposited in the early second century CE, but deeper ceramic bearing contexts stretched back through the first millennium BCE. A single early seventh millennium BCE date associated with stone flakes underlay the site, representing the oldest date recovered from an archaeological context in the region. The archaeological evidence this study presents suggests the entire landscape has undergone continual alteration for numerous millennia, but much of the landscape\u27s current form represents Atlantic influences and more recent historical dynamics and transformations of the colonial and post-colonial periods. I examine this fragmented landscape using satellite remote sensing, archaeological pedestrian survey, diagnostic artifact analyses, and limited test excavations to identify and assess features and transformative processes

An integrated study of earth resources in the State of California using remote sensing techniques

The author has identified the following significant results. The supply, demand, and impact relationships of California's water resources as exemplified by the Feather River project and other aspects of the California Water Plan are discussed