The articles on Multimodal biometrics have focused on data sources. The articles looked at multi-sensor fusion strategy, multi-algorithm strategies and multi-instance fusion. Multi-sensor fusion strategy utilises a single biometric trait captured using multiple sensors in order to extract diverse information from the image. While, multi-algorithm system uses only a single biometric capturing device (single sensor) to obtain raw data and then the raw biometric data is processed using multiple algorithms. It is clear that multi-algorithm systems are cheaper and more convenient for customers than multi-sensor as multi-algorithm systems do not require the use of additional biometric capturing devices. The customers are not required to interact with multiple capturing devices. However, multi-algorithm fusion requires the introduction of a new feature extractor and/or matcher modules which may increase the computational requirements of the system. Multi-instance fusion strategy utilises multiple instances of the same body trait such as the left and right index fingers of an individual may be used to verify an individual’s identity. Multi-instance fusion strategy overcome the limitations of both multi-sensor and multi-algorithms as Multi-instance does not introduce new sensors nor does it require the development of new feature extraction and matching algorithms.
In this article we introduce multi-modal fusion strategy. Multi-modal fusion strategy utilises multiple body trait to establish identity. For example, multimodal biometric systems can utilize face and voice features to establish the identity of an individual. Uncorrected biometric characteristics are expected to result in better improvement in performance than correlated biometric traits. Voice and lip movement are example of correlated traits while fingerprint and iris are examples of uncorrelated biometric traits. The identification accuracy can be significantly improved by utilizing an increasing number of biometric characteristics. The number of biometric characteristics used in a specific application will be restricted by practical considerations such as the cost of deployment, enrollment time, throughput time, expected error rate, user habituation issues. Generally, the main limitations of multi-modal are cost and the development of appropriate user interfaces.
Multi-instance overcomes the limitations of both multi-sensor and multi-algorithms. Multi-instance fusion strategy has the limitations that it may be slow and may result in poor customer satisfaction. Researchers at BRL highlights that the limitations of multi-instances can be simply overcome by a new sensor arrangement in order to facilitate the simultaneous capture of the various units/instances. Multi-instance fusion strategy is advantageous for individuals whose biometric characteristics cannot be reliably captured due to inherent problems. For example, an Automated Fingerprint Identification Systems (AFIS), may benefit from sensors that are able to rapidly acquire impressions of all ten fingers. NBS researchers understand that a single finger may not be a sufficient discrirninator for a person having dry skin or bad fingerprints. Therefore, the integration of evidence across multiple fingers may serve as a good discriminator. Similarly, an iris system may not be able to image significant portions of a person’s iris due to drooping eyelids.
Other fields – Information fusion continue to be used in a diversity of scientific fields such as Object detection. Many applications attempt to detect and establish the trajectories of objects based on the evidence supplied by multiple image modalities. The fusion of visible and non-visible information pertaining to different wavelengths in the electromagnetic spectrum such as radar and infrared images, thermal and visible spectrum images may assist in estimating the location and kinematic features of objects such a squad of soldiers in a night-time battlefield. These applications rely on image fusion methodologies to combine multiple modalities.
Our researchers within BRL at Namibia Biometric Systems will continue to examine the levels of fusion that are possible in a multimodal biometric system in the next article. In addition, our researchers at BRL would like to thank all the readers of the articles from Namibia, Africa and world wide.
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