A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%

A novel smartphone scleral-image based tool for assessing jaundice in decompensated cirrhosis patients

BACKGROUND AND AIM: Serum bilirubin is an established marker of liver disease. Reliable tools for non-invasive assessment of jaundice in cirrhosis patients, at risk of clinical decompensation, are highly desirable. While smartphone-based imaging has been described in neonatal jaundice, it has not been investigated in advanced cirrhosis patients. METHODS: We included 46 hospitalized patients with acute cirrhosis decompensation and jaundice. Scleral images using an Android smartphone were taken to derive "Scleral Color Values (SCV)," which were matched with same day serum bilirubin measurements. In 29 patients, repeat SCV and bilirubin measurements were performed over time. We analyzed the relationship of SCV and its dynamics with serum bilirubin, clinical scores, and patient outcomes. RESULTS: Of 46 patients, 26 (57%) had alcoholic hepatitis as the decompensation precipitant. Seven patients died during admission; a further 12 following hospital discharge. SCV had an excellent linear correlation with serum bilirubin (rho = 0.90, P < 0.001); changes in SCV and serum bilirubin across different time points, were also closely associated (rho = 0.77, P < 0.001). SCV correlated significantly with CLIF Consortium Acute Decompensation score (rho = 0.38, P < 0.001) and grade of Acute-on-Chronic Liver Failure (rho = 0.42, P = 0.039). SCV was higher in patients who died, however, not significantly (86.1 [IQR 83.0-89.7] vs 82.3 [IQR 78.5-83.3], P = 0.22). The associations of SCV with clinical parameters mirrored those of serum bilirubin. CONCLUSION: Smartphone-based assessment of jaundice shows excellent concordance with serum bilirubin and is associated with clinical parameters in acute cirrhosis decompensation. This approach offers promise for remote assessment of cirrhosis patients at-risk of decompensation, post hospital discharge

DEEP LEARNING METHODS FOR BIOMETRIC RECOGNITION BASED ON EYE INFORMATION

The accuracy of ocular biometric systems is critically dependent on the image acquisition conditions and segmentation methods. To minimize recognition error robust segmentation algorithms are required. Among all ocular traits, iris got the most attention due to high recognition accuracy. New modalities such as sclera blood vessels and periocular region were also proposed as autonomous (or iris-complementary) modalities. In this work we tackle ocular segmentation and recognition problems using deep learning methods, which represent state-of-the-art in many computer vision related tasks. We individually evaluate three recognition pipelines based on different ocular modalities (sclera blood vessels, periocular region, iris). The pipelines are then fused into a single biometric system and its performance is evaluated. The main focus is sclera recognition in the scope of which we i) create a new dataset named SBVPI, ii) propose and evaluate segmentation approaches, which won the first place on SS(ER)BC competitions, and iii) develop and evaluate the rest of the sclera-based recognition pipeline. The next contribution of this work is multi-class eye segmentation technique, which gives promising results. We also propose and evaluate deep learning pipeline for periocular recognition. For iris recognition we use an existing pipeline and evaluate it on our dataset. With deep learning we achieve promising recognition results for each individual modality. We further improve recognition accuracy with multi-modal fusion of all three modalities

Deep learning methods for biometric recognition based on eye information

The accuracy of ocular biometric systems is critically dependent on the image acquisition conditions and segmentation methods. To minimize recognition error robust segmentation algorithms are required. Among all ocular traits, iris got the most attention due to high recognition accuracy. New modalities such as sclera blood vessels and periocular region were also proposed as autonomous (or iris-complementary) modalities. In this work we tackle ocular segmentation and recognition problems using deep learning methods, which represent state-of-the-art in many computer vision related tasks. We individually evaluate three recognition pipelines based on different ocular modalities (sclera blood vessels, periocular region, iris). The pipelines are then fused into a single biometric system and its performance is evaluated. The main focus is sclera recognition in the scope of which we i) create a new dataset named SBVPI, ii) propose and evaluate segmentation approaches, which won the first place on SS(ER)BC competitions, and iii) develop and evaluate the rest of the sclera-based recognition pipeline. The next contribution of this work is multi-class eye segmentation technique, which gives promising results. We also propose and evaluate deep learning pipeline for periocular recognition. For iris recognition we use an existing pipeline and evaluate it on our dataset. With deep learning we achieve promising recognition results for each individual modality. We further improve recognition accuracy with multi-modal fusion of all three modalities

DEEP LEARNING METHODS FOR BIOMETRIC RECOGNITION BASED ON EYE INFORMATION

The accuracy of ocular biometric systems is critically dependent on the image acquisition conditions and segmentation methods. To minimize recognition error robust segmentation algorithms are required. Among all ocular traits, iris got the most attention due to high recognition accuracy. New modalities such as sclera blood vessels and periocular region were also proposed as autonomous (or iris-complementary) modalities. In this work we tackle ocular segmentation and recognition problems using deep learning methods, which represent state-of-the-art in many computer vision related tasks. We individually evaluate three recognition pipelines based on different ocular modalities (sclera blood vessels, periocular region, iris). The pipelines are then fused into a single biometric system and its performance is evaluated. The main focus is sclera recognition in the scope of which we i) create a new dataset named SBVPI, ii) propose and evaluate segmentation approaches, which won the first place on SS(ER)BC competitions, and iii) develop and evaluate the rest of the sclera-based recognition pipeline. The next contribution of this work is multi-class eye segmentation technique, which gives promising results. We also propose and evaluate deep learning pipeline for periocular recognition. For iris recognition we use an existing pipeline and evaluate it on our dataset. With deep learning we achieve promising recognition results for each individual modality. We further improve recognition accuracy with multi-modal fusion of all three modalities