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To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training(More)
Personal identity verification based on biometrics has received increasing attention since it allows reliable au-thentication through intrinsic characteristics, such as face, voice, iris, fingerprint, and gait. Particularly, face recognition techniques have been used in a number of applications, such as security surveillance, access control, crime solving,(More)
Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample(More)
In this paper, we present an algorithm to detect the presence of diabetic retinopathy (DR)-related lesions from fundus images based on a common analytical approach that is capable of identifying both red and bright lesions without requiring specific pre- or postprocessing. Our solution constructs a visual word dictionary representing points of interest(More)
Contemporary Vision and Pattern Recognition problems such as face recognition, fingerprinting identification , image categorization, and DNA sequencing often have an arbitrarily large number of classes and properties to consider. To deal with such complex problems using just one feature descriptor is a difficult task and feature fusion may become mandatory.(More)
Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a(More)
Digital images are everywhere—from our cell phones to the pages of our online news sites. How we choose to use digital image processing raises a surprising host of legal and ethical questions that we must address. What are the ramifications of hiding data within an innocent image? Is this an intentional security practice when used legitimately,(More)
Contemporary Vision and Pattern Recognition problems such as face recognition, fingerprinting identification, image categoriza-tion, and DNA sequencing often have an arbitrarily large number of classes and properties to consider. To deal with such complex problems using just one feature descriptor is a difficult task and feature fusion may become mandatory.(More)
—Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or " spoofed ") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems,(More)
Recognition problems in computer vision often benefit from a fusion of different algorithms and/or sensors, with score level fusion being among the most widely used fusion approaches. Choosing an appropriate score normalization technique before fusion is a fundamentally difficult problem because of the disparate nature of the underlying distributions of(More)