A Survey of Super-Resolution in Iris Biometrics With Evaluation of Dictionary-Learning

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matchingThis work was supported by the EU COST Action under Grant IC1106. The work of F. Alonso-Fernandez and J. Bigun was supported in part by the Swedish Research Council, in part by the Swedish Innovation Agency, and in part by the Swedish Knowledge Foundation through the CAISR/SIDUS-AIR projects. The work of J. Fierrez was supported by the Spanish MINECO/FEDER through the CogniMetrics Project under Grant TEC2015-70627-R. The authors acknowledge the Halmstad University Library for its support with the open access fee

Feature-domain super-resolution framework for Gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics

Neptune at Summer Solstice: Zonal Mean Temperatures from Ground-Based Observations 2003-2007

Imaging and spectroscopy of Neptune's thermal infrared emission is used to assess seasonal changes in Neptune's zonal mean temperatures between Voyager-2 observations (1989, heliocentric longitude Ls=236) and southern summer solstice (2005, Ls=270). Our aim was to analyse imaging and spectroscopy from multiple different sources using a single self-consistent radiative-transfer model to assess the magnitude of seasonal variability. Globally-averaged stratospheric temperatures measured from methane emission tend towards a quasi-isothermal structure (158-164 K) above the 0.1-mbar level, and are found to be consistent with spacecraft observations of AKARI. This remarkable consistency, despite very different observing conditions, suggests that stratospheric temporal variability, if present, is $\pm$5 K at 1 mbar and $\pm$3 K at 0.1 mbar during this solstice period. Conversely, ethane emission is highly variable, with abundance determinations varying by more than a factor of two. The retrieved C2H6 abundances are extremely sensitive to the details of the T(p) derivation. Stratospheric temperatures and ethane are found to be latitudinally uniform away from the south pole (assuming a latitudinally-uniform distribution of stratospheric methane). At low and midlatitudes, comparisons of synthetic Voyager-era images with solstice-era observations suggest that tropospheric zonal temperatures are unchanged since the Voyager 2 encounter, with cool mid-latitudes and a warm equator and pole. A re-analysis of Voyager/IRIS 25-50 {\mu}m mapping of tropospheric temperatures and para-hydrogen disequilibrium suggests a symmetric meridional circulation with cold air rising at mid-latitudes (sub-equilibrium para-H2 conditions) and warm air sinking at the equator and poles (super-equilibrium para-H2 conditions). The most significant atmospheric changes are associated with the polar vortex (absent in 1989).Comment: 35 pages, 19 figures. Accepted for publication in Icaru

Geomagnetic disturbances on ground associated with particle precipitation during SC

We have examined several cases of magnetosphere compression by solar wind pressure pulses using a set of instruments located in the noon sector of auroral zone. We have found that the increase in riometric absorption (sudden commencement absorption, SCA) occurred simultaneously with the beginning of negative or positive magnetic variations and broadband enhancement of magnetic activity in the frequency range above 0.1 Hz. Since magnetic variations were observed before the step-like increase of magnetic field at equatorial station (main impulse, MI), the negative declinations resembled the so-called preliminary impulse, PI. In this paper a mechanism for the generation of PI is introduced whereby PI's generation is linked to SCA – associated precipitation and the local enhancement of ionospheric conductivity leading to the reconstruction of the ionospheric current system prior to MI. Calculation showed that PI polarity depends on orientation of the background electric field and location of the observation point relative to ionospheric irregularity. For one case of direct measurements of electric field in the place where the ionospheric irregularity was present, the sign of calculated disturbance corresponded to the observed one. High-resolution measurements on IRIS facility and meridional chain of the induction magnetometers are utilized for the accurate timing of the impact of solar wind irregularity on the magnetopause

Fusion Iris and Periocular Recognitions in Non-Cooperative Environment

The performance of iris recognition in non-cooperative environment can be negatively impacted when the resolution of the iris images is low which results in failure to determine the eye center, limbic and pupillary boundary of the iris segmentation. Hence, a combination with periocular features is suggested to increase the authenticity of the recognition system. However, the texture feature of periocular can be easily affected by a background complication while the colour feature of periocular is still limited to spatial information and quantization effects. This happens due to different distances between the sensor and the subject during the iris acquisition stage as well as image size and orientation. The proposed method of periocular feature extraction consists of a combination of rotation invariant uniform local binary pattern to select the texture features and a method of color moment to select the color features. Besides, a hue-saturation-value channel is selected to avoid loss of discriminative information in the eye image. The proposed method which consists of combination between texture and colour features provides the highest accuracy for the periocular recognition with more than 71.5% for the UBIRIS.v2 dataset and 85.7% for the UBIPr dataset. For the fusion recognitions, the proposed method achieved the highest accuracy with more than 85.9% for the UBIRIS.v2 dataset and 89.7% for the UBIPr dataset

On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems

The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives in dimension reduction, preservation of variance and of pairwise relative distances. Investigations of their asymptotic correlation as well as numerical experiments show that a projection does usually not satisfy both objectives at once. In a standard classification problem we determine projections on the input data that balance the objectives and compare subsequent results. Next, we extend our application of orthogonal projections to deep learning tasks and introduce a general framework of augmented target loss functions. These loss functions integrate additional information via transformations and projections of the target data. In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy