Soft Computing and Image Analysis Lab
This version of the database is composed of 1877 images collected from 241 persons during September, 2004 in two distinct sessions. Its most relevant characteristic is to incorporate images with several noise factors, simulating less constrained image acquisition environments. This enables the evaluation of the robustness of iris recognition methods.
For the first image capture session, the enrollment one, we tried to minimize noise factors, specially those relative to reflections, luminosity and contrast, having installed image capture framework inside a dark room.
In the second session we changed the capture place in order to introduce natural luminosity factor. This propitiates the appearance of heterogeneous images with respect to reflections, contrast, luminosity and focus problems. Images collected at this stage simulate the ones captured by a vision system without or with minimal active participation from the subjects, adding several noise problems. These images will be on the recognition stage compared to the ones collected during first session.
In 2004 we released the UBIRIS.v1 database. Our purpose was to simulate less constrained imaging processes and acquire visible wavelength images with several types of data occluding the iris rings (considered noise). A large number of experiments were conducted on this database and reported in the literature, although the realism of its noise factors received some criticisms. This was a major motivation for the development of a new version of the database (UBIRIS.v2) in which the images were actually captured on non-constrained conditions (at-a-distance, on-the-move and on the visible wavelength), with corresponding more realistic noise factors and has over 11 000 images.
The major purpose of the UBIRIS.v2 database is to constitute a new tool to evaluate the feasibility of visible wavelength iris recognition under far from ideal imaging conditions. In this scope, the various types of non-ideal images, imaging distances, subject perspectives and lighting conditions existent on this database could be of strong utility in the specification of the visible wavelength iris recognition feasibility and constraints.
We built a new data set able to be used in periocular recognition experiments in non-controlled acquisition conditions and setups. We intended to embed on this dataset as much data variability factors as we could: data was acquired from highly different subject-camera distance, under distinct types and levels of illumination, poses and occlusions. Additionally, the manual annotation of dataset, including the ROI and location of essential landmarks, is made available on same website. The ground truth generation was observer centric which means it was performed with the process of observing the subject's image and assign the pose angle.
For the sake of accuracy, present biometric recognition systems require that subjects stand close to the imaging camera for a period of several seconds until the data is captured. This cooperative behavior is required to capture images with enough quality for the recognition task. However, it simultaneously restricts the range of domains where biometric recognition can be applied, especially those where the subjects cooperation is not expectable (e.g., criminal/terrorist seek, missing children).
The main focus of the UBEAR database is to supply a data set of ear images captured in uncontrolled environments and under unconstrained protocols, so that images appear to be captured in real-world conditions, i.e., with subjects on-the-move and without requiring them any particular care about occlusions of the ears and poses. The goal is that this data sets constitutes a new resource for the development of more robust biometric recognition systems.
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