Multimodal Biometrics Fusion using Multi-Instance

The previous two articles focused on multi-sensor fusion strategy and multi-algorithm strategies. Multi-sensor fusion strategy utilises a single biometric trait captured using multiple sensors in order to extract diverse information from the image. On the other hand, multi-algorithm systems circumvent the limitations of multi-sensor as multi-algorithm systems do not require the use of additional biometric capturing devices and the associated costs. In addition, the users are not required to interact with multiple capturing devices and thus enhance user convenience. However, NBS researcher at the Biometric Research Laboratory, BRL, are keen to highlight multi-algorithm fusion requires the introduction of a new feature extractor and/or matcher modules which may increase the computational requirements of the system.

In this article we introduce multi-instance fusion strategy. Multi-instance fusion strategy utilises multiple instances of the same body trait. This can be illustrated using multiple examples, the left and right index fingers of an individual may be used to verify an individual’s identity. However, multi-instance fusion strategy does not impose the introduction of new sensors nor does it entail the development of new feature extraction and matching algorithms. Similarly, the left and right iris of an individual does not require the introduction of new sensors or multiple algorithms which introduces associated costs and computational complexity. 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

Multimodal Biometrics Fusion using Multi-Algorithms

The previous article focused on multi-sensor fusion strategy where a single biometric trait is captured using multiple sensors in order to extract diverse information from the image. A multi-sensor fusion strategy can be implemented as follows using face and fingerprint traits:

Face Biometric System – a system may capture the two dimensional texture content of a person’s face using a 2D camera and a three dimensional surface shape of the face using a 3D camera. The use of multiple sensors, in some instances, can result in the acquisition of complementary information that can enhance the recognition ability of the system. The performance of a 2D face matching system can be improved by utilizing the shape information presented by 3D range images.

Fingerprint Biometric System – a system may capture an individual’s fingerprint images using an optical fingerprint sensor which involves capturing a digital image of the print using visible light ( a specialized digital camera) and capacitive fingerprint sensor which use principles associated with capacitance in order to form fingerprint images. The two sensors provide complementary information and therefore results in enhanced matching accuracy.

Although a multi-sensor strategy has the essential benefits of enhanced accuracy if implemented corrected, the introduction of additional biometric capturing equipments such as a 3D camera to measure the facial surface variation and optical sensor for fingerprint increases the cost of the multimodal biometric system.

Unlike multi-sensor systems highlighted above and in the previous article, in this article we consider a multi-algorithm fusion strategy. 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. Multi-algorithm fusion strategy can be applied to an Automated Fingerprint Identification System (AFIS) as follows:

AFIS – the same fingerprint image captured using an optical sensor can independently be processed by texture-based fingerprint algorithm and a minutiae-based fingerprint algorithm in order to extract diverse feature sets that can improve the performance of the system.

Multi-algorithm systems do not require the use of additional biometric capturing devices and therefore overcomes the limitations of the Multi-sensor system such as costs. Multi-algorithm strategy can be cost-effective. Furthermore, the user is not required to interact with multiple capturing devices and therefore multi-sensor fusion strategy can enhance user convenience. However, multi-sensor fusion requires the introduction of new feature extractor and/or matcher modules which may increase the computational requirements of the system.

Other fields – Information fusion has also been used in a diversity of scientific fields such as Robotics for navigation. Generally a robot is fitted with a variety of sound, light, image and sensors that allow it to record its environment. In order to determine a suitable action such as move right or tilt camera at a certain angle, the data acquired using these multiple sensors are processed simultaneously.


Our researchers within Biometric Research Laboratory, 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.

More information on the implementation of biometrics based solutions can be requested from

The Fifth Essential for Biometrics Success

The previous articles have outlined four main essentials for biometrics success as align with the goals of the organization, considering and address biometrics privacy concerns, surveying the users and sticking to the project plan. The four essentials for biometrics successes can be summarised as follows:

  • Aims and Objectives – It is critical that the aims and objectives of the organisations are aligned with the goals of biometrics within the organisation in such a way that biometrics becomes a vehicle for the organisation to achieve its goals. Biometrics must be an integral part of the organisation’s success story and must not be thought of as a black box. The complexities of biometrics must not be undermined. The lack of understanding with regards to biometrics is the core reason for poor project design.
  • Considering and addressing privacy concerns – It is vital that biometrics is implemented in the best interests of the users and the success of the organisation. Biometrics aims to protect human beings and their interests from imposters. Therefore, the implementation of biometrics must enhance human rights. However, the lack of understanding on biometrics related issues can result in violation of the aims and objectives of biometrics such as protecting human beings. Biometrics must not be forced upon users.
  • User Survey – It is essential that the users of the biometric system are surveyed. User survey is likely to maximise user acceptance and user comfort. User survey will outline both the positives and negatives from the potential users. The outlined limitations must be addressed to ensure maximum user acceptance. The users are likely to accept the implemented biometrics solution if they are an integral part of the biometric project. Implementation of a biometrics system is not about buying a biometric identification device and connecting to your computer. The users’ concerns must never be dismissed due to their limited knowledge.
  • Sticking to the biometrics implementation plan – It is a must that critical time is spent on coming up with a strong implementation plan and that everyone involved with the project understand the implementation plan. Many organisations fail to understand the necessary steps required for a strong biometrics implementation plan and thus are not prepared for the complex risks associated with implementing such a big project. However, having a strong biometrics implementation plan is not a guarantee that the implementation will be successful. Sticking to a strong biometrics implementation plan is key.

This article focuses on the fifth essential for biometrics success on project implementation, flexibility. For most project managers been flexible is thought of as the opposite of sticking to the plan. Flexibility must be thought of the corollary to “sticking to the biometrics implementation plan”. Any biometrics project plan must be flexible enough without deviating from the final goal. Lack of flexibility in the plan can be almost as damaging to the success of a project as a total lack of a plan. In the ideal world, the circumstances of your biometrics project would match the assumptions made during the planning process and every detail of the plan would execute the way it was planned on paper. Of course we don’t live in a perfect world and thus when implementing a technology that is as new as biometrics, the likelihood of everything happening exactly the way we expect are minimal.

The research work conducted by researchers at Biometric Research Laboratory (BRL) within Namibia Biometric Systems (NBS) shows that most organisations pay very little considerations to (i) user privacy concerns, (ii) aligning biometrics with the organisation’s aims and objectives (iii) survey the users (iv) limited time is spent on planning for the implementation of biometrics and (v) planning for flexibility within any biometrics project without deviating from the goals of the project is essential.

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Behavioral Biometric Applications

Although biometrics refers to the identification of an individual based on his/her physiological or behavioural characteristics. That is, relies on “something which you are or you do” to make personal identification and therefore offers a better solution for identification. Very few applications employ behavioural biometrics. The behavioural characteristics are actions carried out by a person in a characteristic way and include signature, voice pattern, keystroke sequences, gait (the body movement while walking), lip movement, and blinking, though these are naturally dependent on physical characteristics.

Some types of behavioural biometrics can identify individuals through the person’s interaction with a system entirely unrelated to identification or authentication. Examples include driving, typing, or even just watching how you move a mouse to accomplish routine computer tasks. Depending on the measurement, the sample size, and the reliability of the specific biometric, these identification events can be more or less accurate.

It is important to realise that the accuracy specifications of a biometric system may vary depending on the application. Sometimes accuracy is not critical to a particular use of biometrics and the more important concern is to prevent the biometric measurement from having any significant impact on the user. For example, imagine a biometric system built into a family car. The system watches how you drive, brake, signal and even steer so it knows and understands who is behind the wheel. Since the list of enrolled drivers is small, the system doesn’t have to make very many comparisons and if the system’s purpose is to help decide what music to play from the on-board mp3 collection, making the wrong decision won’t have all that big an impact. Interactive biometrics can be designed to protect families. Continuing with the driving scenario, the biometric system can be designed to perform more complex decisions such as

  • Maintain a list of drivers insurance to drivers the car. Therefore, the car can only start once it verifies that the driver is insured to drive the car.
  • Ensure that there is someone of adult size in the passenger seat when a teenager or newly qualified driver is driving.
  • The inboard cellular phone can start calling the owner of the car when someone completely unknown seems to be driving the car.
  • The car can signal should the driver start to fall asleep behind the wheels. This feature is vital in reducing road accidents.

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