How Automated Border Control (ABC) Works

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As outlined in the previous article, Automated Border Control (ABC) is defined as the use of automated or semi-automated systems which can verify the identity of travellers at border crossing points (BCPs), without the need for human intervention. Automated border process is conducted as follows:

  • The traveller inserts the biographical data page of the passport into the passport reader.
  • The passport reader checks security features, extracts information in the Machine Readable Zone (MRZ) and communicates with the chip in the e-Passport to verify the genuineness of the document. The aim of this process is to check that the traveller is carrying a genuine and valid travel document. This is referred to as the Document authentication process.
  • Capture a live biometric image of the traveller, extract relevant features and then compare with the biometric data stored in the chip. This process is similar as in the manual border control where an officer compares the traveller’s face to the picture in the old picture in the passport. The aim of this process is to verify by using biometrics that the travel document belongs to the traveller. This process is referred to identity verification process.
  • The system checks if the traveller is indeed entitled/authorized to cross the border. The biographic data may be checked against available watch list databases. If there is a potential match then the traveller should be directed to an officer. The exact process will depend on the procedures in place within each border management authority.
  • If the verification is successful the E-Gate allows the traveller to cross the border. Otherwise, the traveller is referred to manual control. Therefore, the process must be supervised by qualified border guards. The decision to allow/deny access is based on pre-defined logic, sometimes requiring the intervention of the border guard operating the system. Some ABC systems may allow the recording of traveller’s Entry/Exit data.

In general, an ABC system consists of the following equipments:

  • One or two physical barriers (e-Gates).
  • E-Passport readers: optical recognition of the biographic data page, the MRZ and a radio frequency (RF) reader for communication with the chip.
  • A monitor for displaying instructions in a language understood by travellers.
  • Biometric capture device. This depends on the type of biometric been used.
  • System management hardware and software. Maintenance of both hardware and software are critical for operation of the system.

More information on this article or any of the previous articles can be requested from info@namibiabiometricsystems.com.

Biometrics in Automated Border Control (ABC)

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Border control plays a critical role in securing any Nation’s borders between Ports of Entry (POEs) against all threats. Research analysts at our Biometric Research Laboratory, BRL, approach border control from a risk-based orientation which allows the border control teams to apply Information, Integration and Rapid Response in the most targeted, effective, and efficient manner. Therefore, border control teams must be given the tools and resources to execute their jobs efficiently. Traditional border control methods require the border control officer to compare a picture in the passport to your face. Traditional methods have their limitations such as:

Exhaustion – Imagine being a border control officer comparing old passport photographs to a passenger’s faces all day in addition to checking other vital information. How long will it take before your concentration runs out? Analysts at BRL believe that most passengers are allowed through boarder control without being properly authenticated when staff have been working for a long period of time without sufficient breaks.

Nationality – Imagine being a border control officer analysing passport photographs of different nationalities all day in addition to checking other vital information. Which Nationality would you find easier to analyse? Analysts at BRL suggest that people find it easier to analyse photographs of people from the same nationality as themselves. Therefore, more passengers are likely to pass through border control without been properly authenticated if their being served by someone of different Nationality.

Image – Passport pictures are generally taken in a controlled environment. That is, the lightning and pose are controlled. Imagine being a border control officer analysing passport photographs of passengers with different hairstyles, facial wears and mark-ups all day in addition to checking other vital information. Analysts at BRL highlight that the ability of humans to recognize a human face is a function of hairstyles and facial wear. Therefore, more passengers are likely to pass through border control without been properly authenticated if the passenger’s hairstyle and facial wear are different from the picture in the passport.

The above limitations can be easily be resolved by biometrics at border control by using Automated Border Control (ABC). ABC is defined as the use of automated or semi-automated systems which can verify the identity of travellers at border crossing points (BCPs), without the need for human intervention. Generally, an ABC system consists of one or two physical barriers (e-Gates), document readers, a monitor displaying instructions, a biometric capture device and system management hardware and software.

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

Benefits of Electronic Voting

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There are an increasing number of countries around the world that have implemented or piloted electronic voting (e-voting) and electronic counting (e-counting) systems. Although each country’s experience is different, the increasing adoption of these new technologies is due in part from the recognition that technology may offer benefits over traditional methods of voting and counting. Some of the benefits of e-voting and e-counting may include:

  • E-voting eliminating the cost and logistics involved with paper ballots. The costs of paper ballots must not be undermined. E-voting machines can be used over many elections and therefore the cost is reduced costs significantly if well implemented.
  • E-voting can improve voter identification mechanisms compared to manual traditional methods of voter identification. Traditional methods of voter identification are labour intensive. Voting officials are faced with the monotonous task of manually verifying thousands of voters in challenging working environments. It is expected that the error rate in voter identification increases as the officials get tired. E-voting greatly reduces direct human control and influence in this process.
  • E-voting can make the conduction of complex elections easy and likely to increase voter turnout if implemented with strict guidelines. Introducing e-voting touches the core of the entire electoral process such as the casting and counting of the votes.
  • E-voting eliminating invalid ballots. Paper ballots can result in badly educated voters to cast invalid votes.
  • E-voting can result in faster, more accurate and standardized counting of ballots. In most African countries, the counting of thousands of paper votes is conducted by recent grade 12 graduates who lack the experience and skills required for such a vital national project. In addition, the recruitment process tends to lack strict essential security checks. Thus putting the elections at high risk.
  • E-voting can results in prevention of certain forms of fraud. Many National elections worldwide have been characterised with missing ballots, impersonation voters (where someone steals a voter’s identity and votes on their behalf), etc. On the other hand, fraud in e-voting systems can only be conducted by qualified individuals. The percentage of qualified individuals likely to conduct fraud in e-voting is much smaller compared to the percentage of both qualified and unqualified individual likely to perform fraud on paper voting. The weaknesses of both voting systems must be fully understood in order to minimize fraud.

It is essential that E-voting is implemented with certain guidelines in order to maximise the election outcomes:

  • E-voting must be implemented in a transparent and verifiable manner. Access should be provided for observers in a manner that does not obstruct the electoral process.
  • E-voting and e-counting systems must be easy to understand and use. Stakeholders must be involved in the design of the e-voting and e-counting system.
  • E-voting and e-counting systems must be certified by a qualified, independent body before their use and periodically thereafter.
  • Security measures must be taken to ensure that data cannot be lost in the event of a breakdown, only authorized voters can use the machines, only authorized persons are allowed to access the machines.
  • E-voting and e-counting must be auditable so that it is possible to determine whether they operated correctly. It must be possible to conduct a recount votes. Vote recounts must involve accurate monitored manual recounts of votes cast electronically (e.g., with the paper record representing the basis for legal determination of the vote cast).

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

Levels of Fusion in Multimodal Biometric Systems

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The previous articles on Multimodal biometrics had focused on data sources such as multi-sensor, multi-algorithm, multi-instance and multi-traits. This article will focus on the levels of fusion. The amount of information available to a biometric system drastically decreases as one proceeds from the sensor module to the decision module (matching module). In a multibiometric system, fusion can be accomplished by utilizing the information available in the sensor module, feature module, score module and decision module. The levels of fusion can be broadly classified as fusion prior to matching and fusion after matching.

Fusion prior to matching: This is the fusion of data prior to matching. The integration of information from multiple biometric sources can take place either at the sensor level or at the feature level. The raw data from the various sensors can be integrated or combined in sensor level fusion. Sensor level fusion can only be employed if the multiple sources represent samples of the same biometric characteristics obtained either using a single sensor or different compatible sensors. For example, 2D face images of an individual obtained from several cameras can be combined to form a 3D model of the face. While Feature level fusion refers to the integration of different feature sets extracted from multiple biometric sources. Biometric features used in the feature level fusion can be divided into two set, homogeneous and non-homogeneous. When the feature sets are homogeneous (e.g., multiple measurements of the same biometric characteristic such as a person’s hand geometry), a single resultant feature vector can be calculated as a weighted average of the individual feature vectors. While if the feature sets are non-homogeneous (e.g., features of different biometric modalities such as face and hand geometry), the features can be concatenated into a single feature vector. Feature selection schemes are employed to reduce the dimensionality of the ensuing feature vector. Concatenation is not possible when the feature sets are incompatible.

Fusion after matching: This is the fusion of data post matching. The integration of information after the matcher stage can be divided into four categories: dynamic classifier selection, fusion at the decision level, fusion at the rank level and fusion at the match score level. A dynamic classifier selection scheme chooses the results of that biometric source which is most likely to give the correct decision for the specific input pattern. This is also known as the winner-take-all approach within the biometrics world. Integration of information at the decision level can take place when each biometric system independently makes a decision about the identity of the user (in an identification system) or determines if the claimed identity is true or not (in a verification system). Since most commercial biometric systems provide access to only the final decision output by the system, fusion at the decision level is often the only viable option. When the output of each biometric system is a subset of possible matches (i.e., identities) sorted in decreasing order of confidence, the fusion can be done at the rank level. This is relevant in an identification system where a rank may be assigned to the top matching identities. When each biometric system outputs a match score indicating the proximity of the input data to a template, integration can be done at the match score level.

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 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 info@namibiabiometricsystems.com.

Multimodal Biometrics Fusion using Multi-Algorithms

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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 info@namibiabiometricsystems.com.

The Fifth Essential for Biometrics Success

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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.

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

Behavioral Biometric Applications

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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.

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

Why Biometrics in Military

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The Military is a core unit of most governments authorised to use deadly force and weapons, to support the interests of the country and its citizens. The task of the Military is to defend the State, its citizens and the prosecution of war against another State. In addition, the Military may have additional functions within a society such as the promotion of a political agenda, protecting corporate economic interests, internal population control, construction, emergency services, social ceremonies, guarding important areas, etc. The Military further employees thousands and thousands of tax paying citizens. The role of the Military can be seen as the security spine of many nations. Therefore, it is critical that the security system for the Military is as solid as it can be.

In a unfortunate case that the Military goes to war in response to protecting its citizens from external attacks, sending troops with traditional methods of soldier identification is likely to result in the following limitations:

Soldier Identification – It is likely that some soldiers may get life changing injuries and some may even lose their lives. The Military has commitment toward positive identification and proper burial of the dead. The Military may have sent thousands of troops from different units in addition to allied troops who might not even know each other. The allied troops will require a common way of identification for injured or soldieries who lose their lives in order to avoid intrusion from the enemy. The enemy soldiers are likely to wear the allied troops’ uniform should they try to infiltrate the allied forces. It is clear that traditional methods of Military identification using uniform or dog tags to identify soldiers can easily be breached as dog tags can be lost, stolen or both uniform and dog tags can be switched. Biometric circumvents the limitations mentioned above. Injured soldieries can conveniently and efficiently be identified.

Soldier Counting and Resource Management – It is likely that some of the soldiers may risk been left behind in the war zone if the Military is using traditional means of identification and counting. Biometrics solutions can provide enhanced methods of registering and counting soldiers. For example, soldiers who have lost their lives can quickly be registered by their colleagues by simply clicking their fingerprint on the biometric reader, similarly for injured soldiers and thus the Military can manage their resources efficiently. This is a powerful tool is winning any war.

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

Why Biometrics in Healthcare

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National healthcare systems in the 21st century generally face the following challenges: an aging population; increase in long-term illness; better survival rates due to improved health technologies; shortage of skilled health care workers; health inequality; and increased expectations in the healthcare system to deliver a world class service to its citizens. This has resulted in a need for a healthcare system which efficiently utilizes the interaction between hospital patients and care providers to achieve maximum impact on health outcomes. In addition, the healthcare system must effectively utilize the scarce financial and human resources. This has paved a way for biometrics in hospitals. Some of the benefits of biometric based solutions are as follows:

Patient Focused Care: Generally patients have limited access to their health information as this information is stored in many different locations across the health care system. This health information could be stored in paper based form or a combination of electronic and paper based forms. The main problem of such a system is that it relies on the patient’s knowledge of their health information. This has the risk of diagnosis or treatment errors due to incomplete or inaccurate information being provided at the point of care. It is important to realise the burden put on both patients (in some cases patients might be mentally ill) and the healthcare providers. It is a big task to expect senior citizens to remember all their healthcare history. A patient’s medical record stored in a biometric based system will ensure the right patient health information is electronically available to authorised care provider at the right time to enable informed care and treatment decisions.

Efficiency: Generally patients end up repeating the same information to multiple care providers which could result in patients receiving duplicate treatment. Biometrics based solutions ensures that patients rely on the health system to effectively coordinate their healthcare information. In addition, biometric solutions results in better patient information, better diagnosis which minimize medicine wastage (saving the healthcare money) and provide healthcare management with vital statistical data for better management. Healthcare providers will be able to make more informed decisions as a result of better access to accurate and complete consumer health information. Biometric based healthcare systems provide healthcare providers better access to improved evidence base for treatment decisions.

However, implementing biometric based solution has its own challenges and requires consultations as the costs of getting such a project wrong can be significant.

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