Fingerprint and On-Line Signature Verification Competitions at ICB 2009

This paper describes the objectives, the tasks proposed to the participants and the associated protocols in terms of database and assessment tools of two competitions on fingerprints and on-line signatures. The particularity of the fingerprint competition is to be an on-line competition, for evaluation of fingerprint verification tools such as minutiae extractors and matchers as well as complete systems. This competition will be officialy launched during the ICB conference. The on-line signature competition will test the influence of multi-sessions, environmental conditions (still and mobility) and signature complexity on the performance of complete systems using two datasets extracted from the BioSecure database. Its result will be presented during the ICB conference

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

A Neural Lexical Post-Processor for Improved Neural Predictive Word Recognition

This work presents a neural post-processor introducing lexical knowledge in a neural predictive system for on-line word recognition [4]. Each word is modeled by the natural concatenation of letter-models corresponding to the letters composing it. Successive parts of a word trajectory are this way modeled by different Neural Networks. A dynamical segmentation allows to adjust letter-models to the great variability of handwriting encountered in the words. Our system combines Multilayer Neural Networks and Dynamic Programming with an underlying Left-Right Hidden Markov Model (HMM). Training was performed on 7000 words from 9 writers, leading to already good results in the letter-labelling process. These results are significantly improved, at the word level, thanks to the use of the postprocessor. 1. Introduction Holistic approaches in handwritten word recognition [2, 3] avoid segmentation of words into letters in order to turn the recognizer more tolerant to local distorsions. Unfortunat..

Adaptive Discrimination in an HMM-Based Neural Predictive System for On-Line Word Recognition

In a recent publication [3], we have introduced a neural predictive system for on-line word recognition. Our approach implements a Hidden Markov Model (HMM)-based cooperation of several neural networks. The task of the HMM is to guide the training procedure of neural networks on successive parts of a word. Each word is modeled by the concatenation of letter-models corresponding to the letters composing it. In this article, we present the discriminative training procedures introduced in order to improve the results of our first model. Discriminative training is described at the local level, that is of each extracted parameter vector, and at the global level, that is the level of sequences of labels. We relate this type of training in both cases to the Maximum Mutual Information formalism. Discriminative training was performed on 7000 words from 9 writers, leading to improved results at the character level. Moreover, the use of a neural lexical post-processor (NLPP) gives very good words..