Search Results (8)

Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively. Full article

COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observational study of patients admitted to the University Hospital Split from November 2020 to May 2021. Imaging protocols were based on the assessment of 14 lung zones for both lung ultrasound (LUS) and computed tomography (CT), correlated to a CXR score assessing 6 lung zones. Prediction models for the necessity of mechanical ventilation (MV) or a lethal outcome were developed by combining imaging, biometric, and biochemical parameters. A total of 255 patients with COVID-19 pneumonia were included in the study. Four independent predictors were added to the regression model for the necessity of MV: LUS score, day of the illness, leukocyte count, and cardiovascular disease (χ2 = 29.16, p < 0.001). The model accurately classified 89.9% of cases. For the lethal outcome, only two independent predictors contributed to the regression model: LUS score and patient’s age (χ2 = 48.56, p < 0.001, 93.2% correctly classified). The predictive model identified four key parameters at patient admission which could predict an adverse outcome. Full article

The fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map via ellipse fitting, usually after post-processing. In this application, segmentation is just an intermediate step to the estimation of a parameter of interest. Another possibility is to estimate directly the HC with a regression network. Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate. This observation motivates the present study, where we propose a fair, quantitative comparison of segmentation-based and segmentation-free (i.e., regression) approaches to estimate how far regression-based approaches stand from segmentation approaches. We experiment various convolutional neural networks (CNN) architectures and backbones for both segmentation and regression models and provide estimation results on the HC18 dataset, as well agreement analysis, to support our findings. We also investigate memory usage and computational efficiency to compare both types of approaches. The experimental results demonstrate that even if segmentation-based approaches deliver the most accurate results, regression CNN approaches are actually learning to find prominent features, leading to promising yet improvable HC estimation results. Full article

The study investigated normal macromorphological and ultrasonographic features of the eye and lacrimal gland, as well as normal dacryocystorhinography of the donkey (Equus asinus) in Egypt. A total of 36 donkeys of different ages, weights, and sexes were included in the study: 21 live animals for ultrasonography and dacryocystorhinography, and 15 cadaver skulls for morphological anatomy of the lacrimal apparatus. The ultrasound biometric values of the eye were 33.7 ± 1.7 mm for axial globe length (AGL), 39.8 ± 2.1 mm for globe diameter (GD), 10.8 ± 0.7 mm for lens thickness (LT), 3.2 ± 0.7 mm for anterior chamber depth (ACD), and 19.3 ± 1.6 mm for vitreous chamber depth (VCD). The lacrimal gland was recognized as a hypoechogenic structure with an anechoic core, located at the dorsolateral aspect of the orbit, and ovoid in shape. The mean NLD length was 193.0 ± 9.8 mm by radiography and 206.0 ± 20.4 mm by gross assessment. One NL orifice (NLO) was noticed on each side, with a diameter of 3.0 ± 0.1 mm and located 12.1 ± 2.1 mm from the dorsal commissure of the nostril. These results may act as the baseline for proper management of conditions of the eye and lacrimal apparatus in the donkey in the future. Full article

Given the abundance of chickens in Italy, it is important for veterinarians to know the normal state of chickens’ eyes in order to identify any ophthalmic pathological changes. The aim of this study was to determine the normal values of select ocular parameters and to evaluate conjunctival microflora in two Italian chicken breeds. Sixty-six healthy chickens underwent a complete ophthalmic examination, which included a phenol red thread test (PRTT) for the evaluation of tear production and the assessment of intraocular pressure by rebound tonometry. B-mode ultrasound biometric measurements and conjunctival microflora identification were also performed in twenty-seven chickens. Mean PRTT was 23.77 ± 2.99 mm/15 s in the Livorno breed and 19.95 ± 2.81 mm/15 s in the Siciliana breed. Mean intraocular pressure was 14.3 ± 1.17 mmHg in the Livorno breed and 14.06 ± 1.15 mmHg in the Siciliana breed. Reference ranges for morphometric parameters were reported in the two breeds. Twenty-three chickens (85.18%) were bacteriologically positive. Chlamydia spp. antigen was detected in 14.81% of chickens. No positive cultures were obtained for fungi. Normal reference range values for selected ophthalmic parameters were obtained in clinically healthy chickens, which could facilitate accurate diagnosis and better management of ophthalmic diseases in these animals. Full article

Biometrics such as fingerprints and iris scans has been used in authentication. However, conventional biometrics is vulnerable to identity theft, especially in user-management systems. As a new biometrics without this vulnerability, brain waves have been a focus. In this paper, brain waves (electroencephalograms (EEGs)) were measured from ten experiment subjects. Individual features were extracted from the log power spectra of the EEGs using principal component analysis, and verification was achieved using a support vector machine. It was found that, for the proposed authentication method, the equal error rate (EER) for a single electrode was about 22–32%, and that, for a multiple electrodes, was 4.4% by using the majority decision rule. Furthermore, nonlinear features based on chaos analysis were introduced for feature extraction and then extended to multidimensional ones. By fusing the results of all electrodes when using the proposed multidimensional nonlinear features and the spectral feature, an EER of 0% was achieved. As a result, it was confirmed that individuals can be authenticated using induced brain waves when they are subjected to ultrasounds. Full article

Ultrasound has been trialed in biometric recognition systems for many years, and at present different types of ultrasound fingerprint readers are being produced and integrated in portable devices. An important merit of the ultrasound is its ability to image the internal structure of the hand, which can guarantee improved recognition rates and resistance to spoofing attacks. In addition, ambient noise like changes of illumination, humidity, or temperature, as well as oil or ink stains on the skin do not affect the ultrasound image. In this work, a palmprint recognition system based on ultrasound images is proposed and experimentally validated. The system uses a gel pad to obtain acoustic coupling between the ultrasound probe and the user’s hand. The collected volumetric image is processed to extract 2D palmprints at various under-skin depths. Features are extracted from one of these 2D palmprints using a line-based procedure. Recognition performances of the proposed system were evaluated by performing both verification and identification experiments on a home-made database containing 281 samples collected from 32 different volunteers. An equal error rate of 0.38% and an identification rate of 100% were achieved. These results are very satisfactory, even if obtained with a relatively small database. A discussion on the causes of bad acquisitions is also presented, and a possible solution to further optimize the acquisition system is suggested. Full article

Biometric recognition systems are finding applications in more and more civilian fields because they proved to be reliable and accurate. Among the other technologies, ultrasound has the main merit of acquiring 3D images, which allows it to provide more distinctive features and gives it a high resistance to spoof attacks. This work reviews main research activities devoted to the study and development of ultrasound sensors and systems for biometric recognition purposes. Several transducer technologies and different ultrasound techniques have been experimented on for imaging biometric characteristics like fingerprints, hand vein pattern, palmprint, and hand geometry. In the paper, basic concepts on ultrasound imaging techniques and technologies are briefly recalled and, subsequently, research studies are classified according to the kind of technique used for collecting the ultrasound image. Overall, the overview demonstrates that ultrasound may compete with other technologies in the expanding market of biometrics, as the different commercial fingerprint sensors integrated in portable electronic devices like smartphones or tablets demonstrate. Full article