Biometric authentication is the security procedure, which mainly relies on unique biological features of any specific person for the purpose of verification or unique identification (De Luca et al., 2015). The biometric authentication is utilized for the management of accessing the digital and physical resources like rooms, buildings as well as computing devices. There are various significant technologies of this biometric authentication and all of them are extremely popular for the users (Bhagavatula et al., 2015). The following research report outlines a brief description on the biometric authentication and its technologies. The various challenges or problems with these technologies will be identified in the report and relevant gaps will be identified. Moreover, the future research directions in biometric authentication will also be provided here.
Biometric authentication is considered as one of the safest and the most secured technology for verifying or identifying any specific person (Awasthi & Srivastava, 2013). There are various important and significant technologies of the biometric authentication system. These are given below:
i) Fingerprint Recognition System: The first and the foremost technology of biometric authentication is the fingerprint recognition system (Sayed et al., 2013). It is the automated methodology to identify or confirm the respective identity of the individual based on the basic comparison of any two fingerprints. This is the most utilized biometric solution for the authentication purpose.
ii) Retina Scanning: The second popular technology of biometric authentication is the retina scanning (Klonovs et al., 2013). This eventually produces the significant image of blood vessel patterns within the light sensitive surface that is lining the inner eye of the person. The human retina is the thin tissue that is composed of the neural cells, located on the posterior portions of the specific eyes. Each and every individual’s retina is different or unique and this is because of the complex structure of capillaries, which supply retina with blood (Bhatt & Santhanam, 2013). Hence, it is nearly impossible to get forged or fake data from this scanning.
iii) Iris Recognition System: The third significant technology of this biometric authentication is the iris recognition system (Abo-Zahhad, Ahmed & Abbas, 2014). It is the automatic methodology of biometric identification, which utilizes a mathematical pattern recognition technique on the video images of any one or even both the irises of any person, where the complex patterns are absolutely unique and stable. This type of biometric system uses the technology of video camera with subtle near the infrared illumination for acquiring images of the iris structures (Chen, Pande & Mohapatra, 2014).These are mostly used in the security offices or banks.
iv) Voice Recognition: Another important and significant technology of biometric authentication is voice recognition system (Nandi et al., 2014). This is the core ability of any program or machine for receiving as well as interpreting dictation or even for understanding or carrying out the spoken commands. These systems allow the users for interacting with the technology by only taking with it and hence enabling hands free reminders and requests (Peng et al., 2014). The most popular example of this biometric authentication technology is Siri from Apple.
v) Face Recognition: This is the fifth famous technology of biometric authentication (Roth et al., 2013). This particular system has the capability for verifying or identifying the individual from either a digital image or from the video frame or source. There are various techniques where the facial recognition systems could work easily; however, in general the chosen facial features are being compared from the provided images in the database. The face recognition system is completely on the basis of the presence of artificial intelligence in it (Sizov, Lee & Kinnunen, 2014). This system is utilized within the security systems and hence could be compared to all other biometrics like iris recognition and fingerprint recognition.
There are several important applications of these biometric technologies. The applications are as follows:
i) Identification or Verification of Individual: The most important application of this technology is to identify or verify any specific individual. The criminal investigation is completely dependent on these types of technologies in the entire world (Murillo-Escobar et al., 2015). Previously this was based on paper or labours; however, with the advancement of this technology, people have successfully identified the criminals, without any type of issues.
ii) Duplicate Checking: The next application of biometric authentication technologies is the duplicate checking. The fake or the forged data is being checked and eradicated with the help of this technology and the authorized person is being identified (Fong, Zhuang & Fister, 2013). The security of the physical assets or resources is also maintained easily or promptly with these technologies.
iii) Eradication of Fake Data: Another important application of the biometric authentication technologies is the eradication or elimination of fake data (De Luca et al., 2015). Since, biometric characteristics or features are being identified, there is almost no chance of fake data and only accurate data is identified. This also ensures that the fake data is not present within the data at all.
There are some of the major challenges or problems within the technologies of biometric authentication and they are as follows:
i) Data Integrity: For some of the popular technologies of biometric authentication, there is a high chance the data loses the confidentiality or integrity. The fingerprint recognition system is one of them (Bhagavatula et al., 2015). Often, the data is not 100% accurate and thus the user faces major issues. Since, these types of systems are mostly being utilized in schools, colleges and offices, the employees or the students get the chance to forge the data and the management gets into trouble.
ii) Requirement of Extra Hardware: The next significant challenge in biometric authentication systems is that each and every system requires the implementation of an extra hardware (Awasthi & Srivastava, 2013). Without this hardware, it is not possible to use these systems at any cost. This increases complexity to a major level. Hence, this is often avoided by various schools, colleges and offices.
iii) Highly Expensive: Another popular challenge with the biometric authentication system is that it is extremely costly. Due to the requirement of extra hardware and equipment, the overall expenses are huge and hence it often becomes a major problem to afford them (Sayed et al., 2013). Only the larger or the medium sized organizations could afford these types of technologies.
iv) Updates Not Possible: The significant challenge with the biometric authentication technology is that updates of the data are not possible and hence data could not be altered at all (Bhatt & Santhanam, 2013). The fingerprint, iris or the voice of the individual could not be revoked under any circumstance and hence data theft is possible here.
Although, biometric authentication is extremely popular and important for the users, there are certain gaps that are to be identified for providing better solutions to the users (Abo-Zahhad, Ahmed & Abbas, 2014). The gaps of biometric authentication technologies are as follows:
i) Lack of Trustworthiness: The most significant gap that is present within the biometric authentication technology is the lack of trustworthiness (Chen, Pande & Mohapatra, 2014). This is mainly because of the fake or forged data that is quite common for some of the biometric systems. This lack of trustworthiness should be mitigated subsequently to provide proper and accurate data for the users (Sizov, Lee & Kinnunen, 2014). There are some of the major authentication technologies and these technologies are based on three distinct factors, which are individual knowledge like passwords, individual possession like secured token or physical keys and individual doing like biometric technology implementation. The biometric systems could eventually function without any active input, users’ knowledge as well as the users’ cooperation (Murillo-Escobar et al., 2015). Due to the lack of trustworthiness, the recognition is not always utilized by the users.
ii) Lack of Threat Analysis: The second significant gap that is being identified from the research report is that the biometric authentication technologies do not comprise of the threat analysis (Fong, Zhuang & Fister, 2013). A proper threat analysis is to be conducted in this case and then the threat models for the system could be developed. The significant analysis of the threats is highly needed here; however, the biometric technologies do not comprise of the threat analysis in any case (Nandi et al., 2014). The feasibility of the threats is not present for protecting the resources against any type of issue and thus the problem is to be understood eventually.
iii) Complexity of Hardware Implementation: The next identified gap in this case is the complexity of hardware implementation. There is always a requirement of hardware implementation or hardware installation within the biometric authentication technologies (Peng et al., 2014). Each and every biometric system requires an additional hardware, which often becomes complex and problematic for the users. Moreover, testing, designing and deployment also required with this technique.
The identified gaps often become major issues for the users since they are unable to solve these issues. However, future research directions are present for these identified gaps.
i) For the first identified gap of the lack of trustworthiness, the device could be connected to the Internet of Things (Klonovs et al., 2013). The devices would be having huge memory or processing capabilities and hence, when the devices would be scanning the biometric data of the intended users, there would not chance of fake or forged data. Hence, the first gap would be mitigated.
ii) For the second identified gap of lack of threat analysis, in built firewall will be present. This is extremely important for this issue and hence the threats would not only be detected but also prevented on time (Sayed et al., 2013). Thus, the next gap will also be removed.
iii) Finally, for the final gap of hardware complexity implementation, the scientists have thought of involving Internet of Things technology within the systems (Awasthi & Srivastava, 2013). This would have a sensor and hardware will not be involved and thus the complexity would be removed.
Conclusion
Therefore, from the above discussion, it could be concluded that biometric authentication is being utilized for the core purposes of access control and unique verification. Few technologies are present for this type of authentication. This type of verification is extremely vital for the users, since fake or forged data is almost impossible here. The above research report has clearly identified the relevant technologies and applications for biometric authentication in current days. The most popular technology is the fingerprint recognition technology. The various challenges and problems for this technology are also mentioned here. Moreover, proper gaps are being identified within the research area after discussing about the issues. Finally, the future research directions on the identified gaps are also given in this report.
References
Abo-Zahhad, M., Ahmed, S. M., & Abbas, S. N. (2014). Biometric authentication based on PCG and ECG signals: present status and future directions. Signal, Image and Video Processing, 8(4), 739-751.
Awasthi, A. K., & Srivastava, K. (2013). A biometric authentication scheme for telecare medicine information systems with nonce. Journal of medical systems, 37(5), 9964.
Bhagavatula, R., Ur, B., Iacovino, K., Kywe, S. M., Cranor, L. F., & Savvides, M. (2015). Biometric authentication on iphone and android: Usability, perceptions, and influences on adoption.
Bhatt, S., & Santhanam, T. (2013, February). Keystroke dynamics for biometric authentication—A survey. In Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on (pp. 17-23). IEEE.
Chen, S., Pande, A., & Mohapatra, P. (2014, June). Sensor-assisted facial recognition: an enhanced biometric authentication system for smartphones. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services (pp. 109-122). ACM.
De Luca, A., Hang, A., Von Zezschwitz, E., & Hussmann, H. (2015, April). I feel like I’m taking selfies all day!: towards understanding biometric authentication on smartphones. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1411-1414). ACM.
Fong, S., Zhuang, Y., & Fister, I. (2013). A biometric authentication model using hand gesture images. Biomedical engineering online, 12(1), 111.
Klonovs, J., Petersen, C. K., Olesen, H., & Hammershoj, A. (2013). ID proof on the go: Development of a mobile EEG-based biometric authentication system. IEEE Vehicular Technology Magazine, 8(1), 81-89.
Murillo-Escobar, M. A., Cruz-Hernández, C., Abundiz-Pérez, F., & López-Gutiérrez, R. M. (2015). A robust embedded biometric authentication system based on fingerprint and chaotic encryption. Expert Systems with Applications, 42(21), 8198-8211.
Nandi, S., Roy, S., Dansana, J., Karaa, W. B. A., Ray, R., Chowdhury, S. R., … & Dey, N. (2014). Cellular automata based encrypted ECG-hash code generation: an application in inter human biometric authentication system. International Journal of Computer Network and Information Security, 6(11), 1.
Peng, J., El-Latif, A. A. A., Li, Q., & Niu, X. (2014). Multimodal biometric authentication based on score level fusion of finger biometrics. Optik-International Journal for Light and Electron Optics, 125(23), 6891-6897.
Roth, J., Liu, X., Ross, A., & Metaxas, D. (2013, June). Biometric authentication via keystroke sound. In Biometrics (ICB), 2013 International Conference on (pp. 1-8). IEEE.
Sayed, B., Traore, I., Woungang, I., & Obaidat, M. S. (2013). Biometric authentication using mouse gesture dynamics. IEEE Systems Journal, 7(2), 262-274.
Sizov, A., Lee, K. A., & Kinnunen, T. (2014, August). Unifying probabilistic linear discriminant analysis variants in biometric authentication. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (pp. 464-475). Springer, Berlin, Heidelberg.
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