Namibia Biometric Systems’ Banking Solution

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This article is motivated by the work conducted by researchers within Biometric Research Laboratory, BRL, at Namibia Biometric Systems on biometric solution for banking. The work is motivated by the growing interest in the deployment of biometric solution in banking and to minimize error rates during the implementation phase. Researchers at BRL discovers interesting results in the deployment of biometrics in banking: cash machine cards are becoming a thing of the past following the launch of new biometric Automated Teller Machines, ATMs, which let users withdraw money simply by pressing their biometrics that identifies them from the unique biometric pattern. The research conducted by BRL makes the days of fumbling with desktop card readers, phone authentication and PIN codes a thing of the past. The biometric solution can incorporate a suitable personal biometric scanner to keep your internet banking security firmly under your biometrics which you will never forget. The aim of the research at BRL is to replace the costly chip in bank cards with an individual’s biometrics and eliminates the following limitations:

Card chip production – It costs banks a significant amount of money to pay for the chips used in the current bank cards. The cost of chip production is generally passed down to customers in various ways. Banks have to buy chip from chips manufacturing companies who also need to make a profit.

Bank Card Production – It is important for consumers to release that banks also have to pay card manufacturers for the production of the banking cards who also need to make a profit. It is not a surprise that the cost is passed down to the clients in various forms.

Bank Chip and card integration – the integration of the chip and bank card requires specialised skills to ensure the integrity of the card.

The Return On Investment, ROI, is substantial in banking for 3rd world countries if implemented within suitable guidelines and standards. However, researchers at BRL are keen to emphasise that in every country banks are different and may require specific consultation.

The proposed biometric solution for banking by BRL is characterised as follow:

  • It captures additional information such as demographic data like date of birth, present address, National Identification/ passport, marital status, education, etc.
  • It ensures that each customer has the relevant products such as saving accounts, fixed term deposits, etc.
  • It ensures accurate processing of salary and wage slips.
  • It ensures accurate processing of the senior citizen’s pensions.
  • It ensures a reduced monthly fee which is automatically deducted.

Researchers at BRL have independently identified and assessed the best biometrics for banking solutions using international standards from different vendors. Researchers at BRL are keen to stress the importance of utilizing biometric standards and the best practice when implementing biometric based solution.

More information on the implementation of biometric based solution for banking can be obtained via a requested from info@namibiabiometricsystems.com.

Multimodal Biometrics Fusion using Multi-Traits

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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-traits fusion strategy. Multi-traits fusion strategy utilises multiple body trait to establish identity. For example, multi-traits 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-traits are cost and the development of appropriate user interfaces.

Other fields – Information fusion continue to be used in a diversity of scientific fields such as Land Mine detection. Several types of sensor technologies are being used to detect buried land mines. These include electromagnetic induction (EMI), ground penetrating radar (GPR), infra-red imaging (IR), quadrupole resonance (QR), chemical detectors and sensors of acoustically induced surface vibrations. In many cases, the data presented by these multiple sensors are concurrently used to improve the accuracy of land mine detection algorithms.

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.

More information on the implementation of biometrics based solutions can be requested from info@namibiabiometricsystems.com.

Limitations of using a Single Biometric in applications

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In recent years, there has been a significant surge in the use of biometrics for user – authentication applications such as electronic passports, electronic voting, etc because biometrics-based authentication offers multiple benefits over knowledge and possession-based methods such as password and PIN based systems. It is crucial that such biometrics-based authentication systems are designed to resist different sources of attacks on the system when employed in security-critical applications. Therefore this article outlines some of the inherent limitations of using a single biometrics-based authentication scheme.

Biometric data capture device – The environment in which biometric data is captured during the enrolment phase is usually different from the environment in which the biometric data is captured during the identification or verification phase. The captured biometric data might be noisy or deformed. The source of noise can be due to the followings:

  • Captured voice data can be altered by cold, acute laryngitis viral infection that leads to swelling of the vocal cord, chronic laryngitis can be caused by acid reflux disease due to low grade infections such as infections of the vocal cords in people using inhalers for asthma, voice misuse and overuse as speaking is a physical task that requires coordination of breathing with the use of several muscle groups, etc.
  • Captured iris data can be altered by wearing glasses and various infections to the iris.
  • Captured fingerprint data can be altered by scars, oily fingers, dry, wet or damaged temporally or permanently. It is also important to realise that some people might just have poor fingerprint quality.
  • Face data can be altered by environmental conditions such as variations in light, pose or illumination.

It is not surprising that noisy captured biometric data may be false matched with templates in database resulting in a false rejection or false acceptance.

Distinctiveness – Previous articles outlined the characteristics of a good biometrics such as:

  • Distinctiveness: unique to every individual.
  • Universality: every individual must posses the biometric.
  • Permanence: invariant with time (hardly changes).
  • Collectable: the biometrics must be measurable.
  • Performance: high speed and accuracy.

It is easy to see how the performance of a biometric system can be degraded by noisy data and lack of distinctiveness.

Our researchers within Biometric Research Laboratory, BRL, at Namibia Biometric Systems will outline how a practical biometric system can overcome some of the limitations outlined above in the next article.

More information on the implementation of biometrics based solutions can be requested from info@namibiabiometricsystems.com.

Multimodal Biometrics Fusion using Multi-Instance

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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.

More information on the implementation of biometrics based solutions can be requested from info@namibiabiometricsystems.com.