BIOMETRICS 1 Essay Example

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Novel Features of ACS Technology: Biometrics

Introduction

Numerous things different people including knowledge, relationships, birth date, home address am physical attributes. The aspect of identity is represented through personal story and physical embodiment. In the advancing and innovative world, it is possible to use technology to assert personal identity and attached specific information to an individual. The information can be used for the purpose of assisting an individual such as financial record resulting in accessing loans and other benefits. Personal numbers and names are relatively efficient and offer time rested capability to identify an individual. The use of these provides certain degree of efficiency based on unambiguously bonded, consistent, permanent and unique. However, the advancement of computers and technologies has improved the methods of identification and controlling access. One of such a technology is biometrics. Biometrics utilizes behavioral and physical features through use of devices to collect and store digital data about individual’s identity. Through the use of biometrics, the personal data is converted into digital data making the entire process of identification unambiguous, consistent, permanent, and enables users to access the information at a faster rate compared with other methods.

Biometric Modalities

The employment of biometric technologies is advancing and newer and exotic biometrics such as odor, gait, and ear are compelling (Chowhan & Shinde, 2008). However, the commonly utilized biometric methods are voice, iris, face and fingerprint. These biometric modalities are commonly employed because of consistency, permanence, uniqueness and capturing processes is economical and ergonomic in nature. Other methods related to biometric techniques that have been employed include hand geometry, and vascular information such as finger vein and palm.

Even though there are numerous biometric methods, each of these modalities has different advantages and disadvantages. For example, the use of fingerprints is beneficial because it leaves behind information, which can be used in criminal cases (Nandakumar et al. 2008). Facial images are commonly used and humans can associate easily with facial images meaning the facial features can be integrated into the technological platforms (Rua et al. 2012). In addition, the facial details can be collected from a distance while voice information can be collected and can be associated with behavioral characteristics of an individual (Giot, El-Abed & Rosenberger, 2013). Hence numerous sources of biometric information exist, and the usefulness of any modality depends on the economic and ergonomic expectations.

Even though biometric samples can be physically bonded to use, consistent, permanent and unique, the algorithms and sensors used to acquire and later, analyze the information may be imperfect. Sensors are able to introduce electrical and optical distortion (Emmanuel et al. 2014). Some of the information may be lost while others may be distorted. Machines are very good in fulfilling the requirements of security especially retrieving the information, and the success of the biometric systems depends on the infrastructure in place, and strategies employed to collect and analyze the information.

Biometric Processes

iometric technologies and associated systems rely on numerous discrete processes. The discrete processes include template comparison, template extraction, live capture and enrollment. The aim of enrollment is to collect and store biometric samples, which will be used for future comparisons. The storage of archiving system is to store the information or enabling updating the system based on changing variables. The samples collected should be of high quality and consistent in nature to improve the matching performance capability (

Image archive e.g. finger print

BChowhan & Shinde, 2008). Live capture is the ability to capture information from an individual based on the archived information enabling identification of the individual. Template extraction analyses the biometric samples resulting in yielding numerical template. The templates generated are stored for future use, and also improve the speed in which the information can be retrieved (Emmanuel et al. 2014). Algorithmic comparisons are employed in comparing the two biometric templates to make appropriate comparisons. It is important to note that the algorithms and methods employed by these different biometric systems are proprietary in nature; hence, different in nature but shares the same ideology. Therefore, it is difficult to mix the systems for the purpose of identification. The following biometric process defines the biometric approach in fulfillment of identification requirements.

SECURITY CONTROL SYSTEM TECHNOLOGY

Archive database

Enrollment

Presentation of hand/finger

Biometric sample e.g. finger prints

Biometric template

Template database

Template extraction

Template storage

Enrollment

SECURITY CONTROL SYSTEM TECHNOLOGY 1SECURITY CONTROL SYSTEM TECHNOLOGY 2SECURITY CONTROL SYSTEM TECHNOLOGY 3

SECURITY CONTROL SYSTEM TECHNOLOGY 4

Template comparison

Search/match

Presentation of hand/finger

Biometric sample

Biometric template

Comparison algorithms

Live capture

Template extraction

Template comparison

SECURITY CONTROL SYSTEM TECHNOLOGY 5SECURITY CONTROL SYSTEM TECHNOLOGY 6SECURITY CONTROL SYSTEM TECHNOLOGY 7

SECURITY CONTROL SYSTEM TECHNOLOGY 8

SECURITY CONTROL SYSTEM TECHNOLOGY 9

Biometric System Accuracy Testing

The importance of the biometric system or the efficiency is premised on its capability to accomplish the assigned duties of identification. In utilization of the biometric system, it may result in false or true match against the biometric sample (Chowhan & Shinde, 2008). The false match rate is defined as the frequency in which the information assessed from different sources are assessed as from the same source while false non match rate shows a sample from the same source but in real sense, the information is assessed from different sources (Henniger & Nikolov, 2013). An effective biometric system should have low rates of false non-matches and false matches, and the results are prompt in nature. In addition, the higher availability of information improves the efficiency of the information because there are basis in which the information can be compared (Phua et al. 2008). It is also important to create a strong system that shields the system from readily available information e.g. information from public systems. Hence the uniqueness and volume of the information including quality improves the efficiency of the biometric system accuracy.

Biometric Applications

The use of biometric system is traced to its use in identification of a suspect in a criminal investigation. With the advancement of powerful computing and image capture technologies; the strategy has changed to highly automated and digitalized system compared with previous processes associated with labor intensive and paper based (Chowhan & Shinde, 2008). The advancement of the technology has enabled biometric systems and searches to be used in different environment and applied differently. Biometric systems are been used in “authentication” for various logical and physical access control applications (Jain & Nandakumar, 2012). In addition, the technology is used in areas where immediate feedback is required such as border control and areas requiring real-time biometric identification requirements.

Biometric applications employ three processes: duplicate checking, identification and verification (Galbally, Marcel & Fierrez, 2014). Verification entails comparing the present information and compared with the biometric sample enable an individual to secure access to a physical asset such as digital asset, room or even a database (Bhatnagar, Wu & Raman, 2012). The use of biometric system in such scenarios is taken as PIN codes or passwords. The use of biometric details in accessing building and assets is important in championing the security requirements of the facility (Mataar et al. 2013). The biometric system can be offered independently or used to support other security systems. For example, it can be used to complement PIN code to ensure the identity of an individual is authenticated before been given permission to utilize resources.

Identification of an individual is a demanding process that requires effective computing performance and biometric algorithm that enables assessing an individual against a larger database. A single database may contain millions of biometric samples, and it is a challenge to compare a single biometric sample with the entire the database (Chowhan & Shinde, 2008). The system employed is “one-to-many” in which live biometrics of an individual captured, are submitted and compared with the millions available (Galbally, Marcel & Fierrez, 2014). Through the process, it is possible to determine truthfulness of persons identifying themselves. The technology may be utilized in public sector applications in which trust is critical such as in law enforcement and criminal investigation. Other areas in which the biometric system can be employed include broader management, intelligence, defense, employment screening checks and visa issuance.

Biometrics system is used to enable duplicate checking. It enables identification of whether an individual is represented more than once in a given database. The significance of the process is to detect fraud or other criminal activities (Chowhan & Shinde, 2008). For example, an individual can enroll in numerous social benefits program, which negates the significance of social welfare system (Emmanuel et al. 2014). The effectiveness of the processes is based on what is called “biometric deduplication.” Thus, authentication is important in fulfilling socioeconomic requirements and through the use of biometric system, it is possible to streamline the process intended to be achieved.

Sensors and Devices

The effectiveness of any technology/system depends on the equipment and strategies in place (Campisi, 2013). Sensors and devices are electronic systems and mechanical system to capture and enroll raw biometric samples, which can be covered and digitized to a biometric template. Numerous types of sensors and devices are utilized in collecting different information (Jain, Nandakumar & Nagar, 2008). For example, voice, iris, face, fingers, these are microphones, iris cameras, digital cameras, fingerprint sensors, respectively. The technology advancement has been witnessed in the sensors and devices segments where multispectral approaches and emitting sensors are gaining adoption instead of capacitive or optical techniques. The effectiveness of these technologies should consider the resolution, contrast, and creating images absent of distortion (Emmanuel et al. 2014). For instance, optical sensor employs light sensor, light source, and prism to capture the required information. Furthermore, the effectiveness of any technology depends on the requirements and expectations of the users, and defines the type of strategy employed. Hence, determination of appropriate sensors and devices are important to the success of the strategy.

In the case of facial images, numerous devices can be used including webcams, pocket cameras and other devices that capture images. Smartphone can also be used to capture facial images but depends on the resolution in terms of pixels (Chowhan & Shinde, 2008). However, numerous challenges exist when collecting and utilizing the facial matcher, which includes background clutter, sharpness, contrast, brightness, subject facial expression, head angel, and consistency of the pose (Emmanuel et al. 2014). These problems can be solved through improving the database enabling collection of images from different angles. Strategic location of facial imagers is important to improve consistency and effectiveness of the identification process.

Continuous improvement of the sensors has also benefitted the iris biometrics. Iris matching is different from other facial recognition systems because it utilizes infrared image (Galbally, Marcel & Fierrez, 2014). The effectiveness of the process depends on the quality of the image performed, and performance of the associated equipments (Chowhan & Shinde, 2008). The technique is not commonly employed because a normal camera cannot capture the image; a specific camera with infrared light capability has to illuminate and filter unwanted wavelengths.

It is easier to record audio and any recording equipment can accomplish the requirement easily. A normal Smartphone or mobile phone can record the information and commonly used in one-to-one verification. The problem of the strategy is environmental limitations, which results in inconsistent and unpredictable outcome. For example, the background information can interfere will the matching and capture process.

System Architecture and Mode of Use

The biometric application comes in different forms based on the assigned task (Galbally, Marcel & Fierrez, 2014). For example a single may utilize the technique in a one-to-one biometric verification system to protect or secure information of an individual; it is commonly used in accomplishing smart phone requirements – it is commonly referred to as “owner based.” “Permission based” system gives the power to the asset controller to grant self access to specific assets: for example, companies usually grant employees permission to access and usage of their data (Mishra, 2010). “Operator-based” requires authorization and trained personnel who are able to collect biometric information, from the persons providing the information; it is commonly employed by law enforcement agencies (Emmanuel et al. 2014). “Kiosk-based” does not require specialization or training and can be accomplished through minimal instruction; for example, automated border control is usually used to accomplish this requirement.

The location of the biometric template and the enrollment system can be located in any location. For example, the live-captured information can be collected from a single location and the information can be accessed from different locations (Chowhan & Shinde, 2008). For instance, a Smartphone may not need a central server but the law enforcement agency needs a central server ion which the information can be kept. Some of the variables considered in determining the effectiveness and use of the application is security, performance and uses of application (Galbally, Marcel & Fierrez, 2014). For instance, one-to-one biometric systems can be contained on the smartcard chip (Gomez-Barrero, Galbally & Fierrez, 2014). However, the different applications may be employed depending on the requirements of the biometric and availability of resources.

The utilization of biometric application raises numerous privacy concerns. For example, the government collects personal information regarding the citizens with the aim of improving physical security, medical and social requirements (Chowhan & Shinde, 2008). The debate exists in the amount of information that a security agency/government can collect and the utilization of the collected information. Such approaches may be seen as forfeiting personal rights to the government (Emmanuel et al. 2014). Private corporations are also collecting, transacting, utilizing and possessing vast information, which are important and may affect the privacy of owners of the information. In turn, the government and private corporations may abuse the personal information, or utilize to generate “selfish” benefits rather that championing the integrity of the information.

The advancement of technology and utilization of Internet, Smartphone, digital cameras and social media has created an environment with voluminous information. The data can be collected easily and grouped which can later be abused. In addition, it is possible to relate the facial images and other information based on the origins of information (Galbally, Marcel & Fierrez, 2014). For instance, it is easier to associate a facial image to personal information including school, name and associates. The information can be used in a good way but chances exist in which the information may be abused (Rua et al. 2012). Therefore, the provision and utilization of such information creates a huge legal debate when it is right or wrong, and the extent in which the information can be used.

Security

Concerns have been raised on the effectiveness of the biometric application in terms of security (Galbally, Marcel & Fierrez, 2014). Few documented accounts of breaching biometric system exist but may be associated with measures that are beyond control or even can be detected. It may be seen as an approach of simulation to satisfy a given view (Chowhan & Shinde, 2008). For example, iris dilation and fingerprint mutilation can be used to avoid identification; however, the mutilation is irreversible while iris dilation may be detected (Emmanuel et al. 2014). Obfuscating and spoofing are other methods that may be used to “cheat” a biometric but it is extremely difficult to accomplish the task without detection. Hence, biometric application can be used to advance the requirements of security with high success rates.

Conclusion

In conclusion, numerous methods of access control system exist with different levels of application and success. Biometric is one of these technologies that use an individual behavior or physical features of an individual. The technology is even used in Smartphone and different agencies across the world are employing the technology. It is difficult to trick the system because the information collected is unique; for example, the iris image and fingerprints are unique in nature and combining these methods creates a stronger security system.

References

Bhatnagar, G., Wu, J., & Raman, B. (2012). Fractional dual tree complex wavelet transform and its application to biometric security during communication and transmission. Future Generation Computer Systems, 28(1), 254-267.

Campisi, P. (2013). Security and Privacy in Biometrics. London: Springer.

Chowhan, S. S., & Shinde, G. N. (2008, May). Iris biometrics recognition application in security management. In Image and Signal Processing, 2008. CISP’08. Congress on (Vol. 1, pp. 661-665). IEEE.

Emmanuel, B. S., Mu’azu, M. B., Sani, S. M., & Garba, S. (2014). A Review of Wavelet-Based Image Processing Methods for Fingerprint Compression in Biometric Application. British Journal of Mathematics & Computer Science, 4(19), 2781.

Galbally, J., Marcel, S., & Fierrez, J. (2014). Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. Image Processing, IEEE Transactions on, 23(2), 710-724.

Giot, R., El-Abed, M., & Rosenberger, C. (2013). Fast computation of the performance evaluation of biometric systems: Application to multibiometrics. Future Generation Computer Systems, 29(3), 788-799.

Gomez-Barrero, M., Galbally, J., & Fierrez, J. (2014). Efficient software attack to multimodal biometric systems and its application to face and iris fusion. Pattern Recognition Letters, 36, 243-253.

Henniger, O., & Nikolov, D. (2013). Extending EMV payment smart cards with biometric on-card verification. In Policies and Research in Identity Management (pp. 121-130). Springer Berlin Heidelberg.

Jain, A. K., & Nandakumar, K. (2012). Biometric Authentication: System Security and User Privacy. IEEE Computer, 45(11), 87-92.

Jain, A. K., Nandakumar, K., & Nagar, A. (2008). Biometric template security. EURASIP Journal on Advances in Signal Processing, 2008, 113.

Mataar, D., Fournier, R., Lachiri, Z., & Nait-Ali, A. (2013). Biometric application and classification of individuals using postural parameters. Int J Comput Technol, 7(2), 580-93.

Mishra, A. (2010). Multimodal biometrics it is: need for future systems. International journal of computer applications, 3(4), 28-33.

Nandakumar, K., Chen, Y., Dass, S. C., & Jain, A. K. (2008). Likelihood ratio-based biometric score fusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2), 342-347.

Phua, K., Chen, J., Dat, T. H., & Shue, L. (2008). Heart sound as a biometric. Pattern Recognition, 41(3), 906-919.

Rua, E. A., Maiorana, E., Castro, J. L. A., & Campisi, P. (2012). Biometric template protection using universal background models: An application to online signature. Information Forensics and Security, IEEE Transactions on, 7(1), 269-282.