Search Results (4)

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collected a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects. We present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes. Full article

The need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biometrics is promising due to it not having finger or palm patterns on the skin surface making the image acquisition process easier. This paper presents a deep learning-based novel low-cost end-to-end contactless wrist vein biometric recognition system. FYO wrist vein dataset was used to train a novel U-Net CNN structure to extract and segment wrist vein patterns effectively. The extracted images were evaluated to have a Dice Coefficient of 0.723. A CNN and Siamese Neural Network were implemented to match wrist vein images obtaining the highest F1-score of 84.7%. The average matching time is less than 3 s on a Raspberry Pi. All the subsystems were integrated with the help of a designed GUI to form a functional end-to-end deep learning-based wrist biometric recognition system. Full article

Nowadays, biometrics based on vein patterns as a trait is a promising technique. Vein patterns satisfy universality, distinctiveness, permanence, performance, and protection against circumvention. However, collectability and acceptability are not completely satisfied. These two properties are directly related to acquisition methods. The acquisition of vein images is usually based on the absorption of near-infrared (NIR) light by the hemoglobin inside the veins, which is higher than in the surrounding tissues. Typically, specific devices are designed to improve the quality of the vein images. However, such devices increase collectability costs and reduce acceptability. This paper focuses on using commercial smartphones with ordinary cameras as potential devices to improve collectability and acceptability. In particular, we use smartphone applications (apps), mainly employed for medical purposes, to acquire images with the smartphone camera and improve the contrast of superficial veins, as if using infrared LEDs. A recognition system has been developed that employs the free IRVeinViewer App to acquire images from wrists and dorsal hands and a feature extraction algorithm based on SIFT (scale-invariant feature transform) with adequate pre- and post-processing stages. The recognition performance has been evaluated with a database composed of 1000 vein images associated to five samples from 20 wrists and 20 dorsal hands, acquired at different times of day, from people of different ages and genders, under five different environmental conditions: day outdoor, indoor with natural light, indoor with natural light and dark homogeneous background, indoor with artificial light, and darkness. The variability of the images acquired in different sessions and under different ambient conditions has a large influence on the recognition rates, such that our results are similar to other systems from the literature that employ specific smartphones and additional light sources. Since reported quality assessment algorithms do not help to reject poorly acquired images, we have evaluated a solution at enrollment and matching that acquires several images subsequently, computes their similarity, and accepts only the samples whose similarity is greater than a threshold. This improves the recognition, and it is practical since our implemented system in Android works in real-time and the usability of the acquisition app is high. Full article

Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR^®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR^®. The results obtained by combining these three elements, TGS-CVBR^®, PIS-CVBR^®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system. Full article