Hyperspectral Data Acquisition and Its Application for Face Recognition

Current face recognition systems are rife with serious challenges in uncontrolled conditions: e.g., unrestrained lighting, pose variations, accessories, etc. Hyperspectral imaging (HI) is typically employed to counter many of those challenges, by incorporating the spectral information within different bands. Although numerous methods based on hyperspectral imaging have been developed for face recognition with promising results, three fundamental challenges remain: 1) low signal to noise ratios and low intensity values in the bands of the hyperspectral image specifically near blue bands; 2) high dimensionality of hyperspectral data; and 3) inter-band misalignment (IBM) correlated with subject motion during data acquisition. This dissertation concentrates mainly on addressing the aforementioned challenges in HI. First, to address low quality of the bands of the hyperspectral image, we utilize a custom light source that has more radiant power at shorter wavelengths and properly adjust camera exposure times corresponding to lower transmittance of the filter and lower radiant power of our light source. Second, the high dimensionality of spectral data imposes limitations on numerical analysis. As such, there is an emerging demand for robust data compression techniques with lows of less relevant information to manage real spectral data. To cope with these challenging problems, we describe a reduced-order data modeling technique based on local proper orthogonal decomposition in order to compute low-dimensional models by projecting high-dimensional clusters onto subspaces spanned by local reduced-order bases. Third, we investigate 11 leading alignment approaches to address IBM correlated with subject motion during data acquisition. To overcome the limitations of the considered alignment approaches, we propose an accurate alignment approach ( A3) by incorporating the strengths of point correspondence and a low-rank model. In addition, we develop two qualitative prediction models to assess the alignment quality of hyperspectral images in determining improved alignment among the conducted alignment approaches. Finally, we show that the proposed alignment approach leads to promising improvement on face recognition performance of a probabilistic linear discriminant analysis approach

On the use of a liquid lens for improving iris images quality in a hyperspectral system

In this paper, we describe how using a liquid lens can improve the quality of iris images acquired by a hyperspectral system. This improvement in the image quality is especially noticeable for systems that scan the iris over a wide range of wavelengths, e.g. visible and near-infrared spectrum. We have tested this approach on the previously developed system able to acquire iris images in the spectral range 480 - 900 nm. The key novelty presented in this paper is represented by the possibility of adaptively adjusting the focus of the imaging system, allowing for chromatic aberration compensation and ensuring a constant image sharpness among all wavelengths. A fast-tunable liquid lens has been placed in front of the chromatically corrected camera objective to adaptively change the overall focus of the imaging system. The findings imply that the device can rapidly perform hyperspectral measurements of the iris over a broad wavelength range ensuring optimal focus for all images

Snapshot hyperspectral imaging : near-infrared image replicating imaging spectrometer and achromatisation of Wollaston prisms

Conventional hyperspectral imaging (HSI) techniques are time-sequential and rely on temporal scanning to capture hyperspectral images. This temporal constraint can limit the application of HSI to static scenes and platforms, where transient and dynamic events are not expected during data capture. The Near-Infrared Image Replicating Imaging Spectrometer (N-IRIS) sensor described in this thesis enables snapshot HSI in the short-wave infrared (SWIR), without the requirement for scanning and operates without rejection in polarised light. It operates in eight wavebands from 1.1μm to 1.7μm with a 2.0° diagonal field-of-view. N-IRIS produces spectral images directly, without the need for prior topographic or image reconstruction. Additional benefits include compactness, robustness, static operation, lower processing overheads, higher signal-to-noise ratio and higher optical throughput with respect to other HSI snapshot sensors generally. This thesis covers the IRIS design process from theoretical concepts to quantitative modelling, culminating in the N-IRIS prototype designed for SWIR imaging. This effort formed the logical step in advancing from peer efforts, which focussed upon the visible wavelengths. After acceptance testing to verify optical parameters, empirical laboratory trials were carried out. This testing focussed on discriminating between common materials within a controlled environment as proof-of-concept. Significance tests were used to provide an initial test of N-IRIS capability in distinguishing materials with respect to using a conventional SWIR broadband sensor. Motivated by the design and assembly of a cost-effective visible IRIS, an innovative solution was developed for the problem of chromatic variation in the splitting angle (CVSA) of Wollaston prisms. CVSA introduces spectral blurring of images. Analytical theory is presented and is illustrated with an example N-IRIS application where a sixfold reduction in dispersion is achieved for wavelengths in the region 400nm to 1.7μm, although the principle is applicable from ultraviolet to thermal-IR wavelengths. Experimental proof of concept is demonstrated and the spectral smearing of an achromatised N-IRIS is shown to be reduced by an order of magnitude. These achromatised prisms can provide benefits to areas beyond hyperspectral imaging, such as microscopy, laser pulse control and spectrometry

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis

[EN] A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0¿6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead¿s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R2 of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.This work has been partially funded by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA Spanish National Institute for Agriculture and Food Research and Technology) through research project RTA2012-00062-004-02, supported by European FEDER funds.Ivorra Martínez, E.; Verdú Amat, S.; Sánchez Salmerón, AJ.; Grau Meló, R.; Barat Baviera, JM. (2016). Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis. Sensors. 16(10):1-14. https://doi.org/10.3390/s16101735S114161

Human retinal oximetry using hyperspectral imaging

The aim of the work reported in this thesis was to investigate the possibility of measuring human retinal oxygen saturation using hyperspectral imaging. A direct non-invasive quantitative mapping of retinal oxygen saturation is enabled by hyperspectral imaging whereby the absorption spectra of oxygenated and deoxygenated haemoglobin are recorded and analysed. Implementation of spectral retinal imaging thus requires ophthalmic instrumentation capable of efficiently recording the requisite spectral data cube. For this purpose, a spectral retinal imager was developed for the first time by integrating a liquid crystal tuneable filter into the illumination system of a conventional fundus camera to enable the recording of narrow-band spectral images in time sequence from 400nm to 700nm. Postprocessing algorithms were developed to enable accurate exploitation of spectral retinal images and overcome the confounding problems associated with this technique due to the erratic eye motion and illumination variation. Several algorithms were developed to provide semi-quantitative and quantitative oxygen saturation measurements. Accurate quantitative measurements necessitated an optical model of light propagation into the retina that takes into account the absorption and scattering of light by red blood cells. To validate the oxygen saturation measurements and algorithms, a model eye was constructed and measurements were compared with gold-standard measurements obtained by a Co-Oximeter. The accuracy of the oxygen saturation measurements was (3.31%± 2.19) for oxygenated blood samples. Clinical trials from healthy and diseased subjects were analysed and oxygen saturation measurements were compared to establish a merit of certain retinal diseases. Oxygen saturation measurements were in agreement with clinician expectations in both veins (48%±9) and arteries (96%±5). We also present in this thesis the development of novel clinical instrument based on IRIS to perform retinal oximetry.Al-baath University, Syri

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert. © 2010 The Author(s

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