Atlas de oftalmoscopia, representando el estado normal y las modificaciones patológicas del fondo del ojo visible con el oftalmoscopio...

Las h. de lám. "Lith. Anst.v. Winckelmann & Sohne (H. Porsch

The BANCA Database and Evaluation Protocol

In this paper we describe the acquisition and content of a new large, realistic and challenging multi-modal database intended for training and testing multi-modal verification systems. The BANCA database was captured in four European languages in two modalities (face and voice). For recording, both high and low quality microphones and cameras were used. The subjects were recorded in three different scenarios, controlled, degraded and adverse over a period of three months. In total 208 people were captured, half men and half women. In this paper we also describe a protocol for evaluating verification algorithms on the database. The database will be made available to the research community through http://www.ee.surrey.ac.uk/Research/VSSP/banca

An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms

In this paper we introduce the facereclib, the first software library that allows to compare a variety of face recognition algorithms on most of the known facial image databases and that permits rapid prototyping of novel ideas and testing of meta-parameters of face recognition algorithms. The facereclib is built on the open source signal processing and machine learning library Bob. It uses well-specified face recognition protocols to ensure that results are comparable and reproducible. We show that the face recognition algorithms implemented in Bob as well as third party face recognition libraries can be used to run face recognition experiments within the framework of the facereclib. As a proof of concept, we execute four different state-of-the-art face recognition algorithms: local Gabor binary pattern histogram sequences (LGBPHS), Gabor graph comparisons with a Gabor phase based similarity measure, inter-session variability modeling (ISV) of DCT block features, and the linear discriminant analysis on two different color channels (LDA-IR) on two different databases: The Good, The Bad, & The Ugly, and the BANCA database, in all cases using their fixed protocols. The results show that there is not one face recognition algorithm that outperforms all others, but rather that the results are strongly dependent on the employed database

Multiple Traits for People Identification

Present biometric systems mostly rely on a single physical or behavioral feature for either identification or verification. However, day to day use of single biometries in massive or uncontrolled scenarios still has several shortcomings. These can be due to complex or unstable hardware settings, to changing environmental conditions or even to immature software procedures: some classification problems are intrinsically hard to solve. Possible spoofing of single biometric features is an additional issue. Last but not least, some features may occasionally lack the requisite of universality. As a consequence, biometric systems based on a single feature often have poor reliability, especially in applications where high security is needed. Multimodal systems, i.e., systems that concurrently exploit multiple features, are a possible way to achieve improved effectiveness and reliability. There are several issues that must be addressed when designing such a system, including the choice of the set of biometric features, the normalization method, the integration schema and the fusion process, and the use of a measure of reliability for each subsystem on a single response basis. This chapter describes the state of the art regarding such issues and sketches some suggestions for future work

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