Multiple classifiers in biometrics. Part 2: Trends and challenges

The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature, key elements, architecture, main methods, and prevalent theory and framework. Part 1 then overviewed the application of MCS to the particular field of multimodal biometric person authentication in the last 25 years, as a prototypical area in which MCS has resulted in important achievements. Here in Part 2 we present in more technical detail recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way. These new MCS architectures exploit input quality measures and pattern-specific particularities that move apart from general population statistics, resulting in robust multimodal biometric systems. Similarly as in Part 1, methods here are described in a general way so they can be applied to other information fusion problems as well. Finally, we also discuss here open challenges in biometrics in which MCS can play a key roleThis work was funded by projects CogniMetrics (TEC2015-70627-R) from MINECO/FEDER and RiskTrakc (JUST-2015-JCOO-AG-1). Part of this work was conducted during a research visit of J.F. to Prof. Ludmila Kuncheva at Bangor University (UK) with STSM funding from COST CA16101 (MULTI-FORESEE

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Compensating User-Specific Information with User-Independent Information in Biometric Authentication Tasks

Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. This is in general a binary classification task because a system either accepts or rejects an identity claim. However, a biometric authentication system contains many users. By recognizing this fact, better decision can be made if user-specific information can be exploited. In this study, we propose to combine user-specific information with user-independent information such that the performance due to exploiting both information sources does not perform worse than either one and in some situations can improve significantly over either one. We show that this technique, motivated by a standard Bayesian framework, is applicable in two levels, i.e., fusion level where multiple (multimodal or intramodal) systems are involved, or, score normalization level, where only a single system is involved. The second approach can be considered a novel score normalization technique that combines both information sources. The fusion technique was tested on 32 fusion experiments whereas the normalization technique was tested on 13 single-system experiments. Both techniques that are originated from the same principal share a major advantage, i.e., due to prior knowledge as supported by experimental evidences, few or almost no free parameter are actually needed in order to employ the mentioned techniques. Previous works in this direction require at least 6 to 10 user-specific client accesses. However, in this work, as few as two user-specific client accesses are needed, hence overcoming the learning problem with extremely few user-specific client samples. Finally, but not the least, a non-exhaustive survey on the state-of-the-arts of incorporating user-specific information in biometric authentication is also presented

Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability

As biometric technology is increasingly deployed, it will be common to replace parts of operational systems with newer designs. The cost and inconvenience of reacquiring enrolled users when a new vendor solution is incorporated makes this approach difficult and many applications will require to deal with information from different sources regularly. These interoperability problems can dramatically affect the performance of biometric systems and thus, they need to be overcome. Here, we describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion algorithms when biometric signals were generated using several biometric devices in mismatched conditions. Quality measures from the raw biometric data are available to allow system adjustment to changing quality conditions due to device changes. This system adjustment is referred to as quality-based conditional processing. The proposed fusion approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. This allows the easy and efficient combination of matching scores from different devices assuming low dependence among modalities. In our system, quality information is used to switch between different system modules depending on the data source (the sensor in our case) and to reject channels with low quality data during the fusion. We compare our fusion approach to a set of rule-based fusion schemes over normalized scores. Results show that the proposed approach outperforms all the rule-based fusion schemes. We also show that with the quality-based channel rejection scheme, an overall improvement of 25% in the equal error rate is obtained.Comment: Published at IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Human

Adapted user-dependent multimodal biometric authentication exploiting general information

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 26.16 (2005): 2628 – 2639, DOI: 10.1016/j.patrec.2005.06.008A novel adapted strategy for combining general and user-dependent knowledge at the decision-level in multimodal biometric authentication is presented. User- independent, user-dependent, and adapted fusion and decision schemes are com- pared by using a bimodal system based on ¯ngerprint and written signature. The adapted approach is shown to outperform the other strategies considered in this pa- per. Exploiting available information for training the fusion function is also shown to be better than using existing information for post-fusion trained decisions.This work has been supported by the Spanish Ministry for Science and Tech- nology under projects TIC2003-09068-C02-01 and TIC2003-08382-C05-01