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Biometric Authentication: Types, Robustness and the Future

Paper Type: Free Essay Subject: Information Technology
Wordcount: 3404 words Published: 7th Dec 2021

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The world is advancing with the technology, and as technology will advance, security too needs to be advanced and hence will play a crucial role. When we think about information security, authentication will play a crucial role in it. Numerous systems make use of biometric authentication such as tablets, mobile phones, and laptops. The authentication may be biometric, which may be our fingerprints, facial recognition, iris scan, or any physiological parameters. In this paper, we will provide a brief introduction about biometrics, types of biometrics, their robustness, and the future scope of biometrics.

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1. Introduction

The assurance of confidentiality, integrity, and availability is the primary concern when we think about information security. When we are talking about security, authentication will play a crucial role, and so biometrics come into play. Biometrics may be any physiological parameter that can be used to authenticate and establish a one-to-one correspondence between an individual and a piece of data. Biometrics provides a soft flush of confidence and security for authentication. Mobile phones use fingerprints of facial recognition to unlock, or some security doors may use an iris scan to let an individual entry. "According to a recent Ping identity survey, 92% of enterprises rank biometrics as an effective to a very effective way to secure identity for the data stored". All the biometrics works in a similar manner, which includes a scanner, computer, and software. The scanner will scan the physiological feature and will detect the required parameter and will send it to the computer. The computer will have sophisticated software that may be dependent on pattern matching software, which will generate a code. That code will be first be taken as input and later will be used for authentication purposes. Usually, multiple samples are taken to improve efficiency. 

2. Types of Biometric Authentication

There are many types of biometric authentication, which may fingerprints, physiological recognition, signatures, or DNA.

2.1 Fingerprints

The way a digital fingerprint biometric may work is the transient way of the old traditional method of fingerprint authentication in which we were required to create a fingerprint impression using a colored ink on a document that was later sent to a fingerprint scanner and used for authentication. In the present, it works digitally, where a scanner uses a light-sensitive microchip to yield an image(figure 1) and sends it to the computer. The computer will use sophisticated pattern-matching software, which will generate a code that will be first used as input and later for authentication purposes[1,2].

Figure 1: Fingerprint

Taken from www.sciencemag.org

2.2 Physiological recognition

The subsections below suggest an apprised overview of mostly used physiological characteristics for the automated recognition of a particular person.

2.2.1 Iris Scan

Iris scan depends on the patterns in the colored part of our iris(figure 2). Iris patterns are very distinct and obtained from a video-based acquisition system. Iris Scan biometric works in a similar manner as other biometrics. A high-resolution grayscale camera takes an image of the eye 10-40cm away, which is then processed through a computer. The computer runs on a sophisticated pattern-matching software which generates a code and thus uses for authentication[1,7].

Figure 2: Iris Scan

Taken from www.computerworld.com

2.2.2 Retina Scan

Retina Scan is very similar to Iris Scan. The whole process which goes on for iris scan, retina scan follows the same. The only difference is that while the image of the eye is being taken, infrared light is passed onto it as retina lies at the rear of our pupil. The camera captures the pattern of blood vessels behind the eye. These patterns are distinctive(figure 3). The image thus obtained goes through a sophisticated pattern-matching software which generates a code and thus uses for authentication purposes[5].

Figure 3: Retina Scan

Taken from www.shuttershock.com

2.2.3 Palm Vein Recognition

Palm vein recognition does not work on the palm just by itself; rather, it depends on the geometry of the arrangement of our vein. Palm vein biometric works in a similar manner as fingerprints and retina scans. The scanner uses infrared light and a microchip the detect vein patterns(figure 4). The patterns thus obtained go through a sophisticated pattern-matching software, which thus generates a code and uses for authentication[4].

Figure 4: Palm Vein Recognition

Taken from www.matratech.com

2.2.4 Ear Recognition

This recognition works in a similar manner as an iris scan. An ear has distinctive marking and patterns which may be complex to understand. A high grayscale camera captures the image of the ear 10-40cm away. This image then gets transfers to the computer, which runs on the sophisticated software that depends on pattern matching software, which generates a code and uses for authentication(figure 5). Such a type of software was firstly produced by French company ART techniques. This recognition is mainly used in law enforcement applications like crime scenes and is still in progress of getting better[2].

Figure 5: Ear Recognition

Taken from www.scienceabc.com

2.2.5 Voice Recognition

Voice recognition does not depend on the pronunciation of speech itself; rather, it depends on the vocal tract, mouth, and nasal cavities, and other speech refining sources of the human body(figure 6). This biometric uses the acoustics visage of speech, which is distinctive. The speech thus obtained from the recorder gets transferred to the computer. The computer then runs through a sophisticated pattern-matching software and generates code which is used for authentication[3].

Figure 6: Voice Recognition

Taken from www.aware.com

2.2.6 Facial Recognition

Facial Recognition Does not depend on the face by itself; rather, it depends on the distinctive facial features like the positioning of eyes, nose, mouth, and distances between them. A high-resolution camera takes an image of the face, which is then resized to a pre-defined sized template, which may range between 3-5KB(figure 7). The template thus obtained gets transferred to the computer, which later runs on sophisticated pattern-matching software and generates the code[8].

Figure 7: Facial Recognition

Taken from www.securityindustry.org

2.3 Signatures

Signature authentication does not depend on the signature itself rather than gesture while making a signature. The gesture is measured by the pressure, direction, acceleration, dimensions, and direction of the strokes. The most significant advantage of the signatures is that it cannot be stolen by any fraudster by just looking at how it was previously written. The information about gestures thus obtained runs through a sophisticated pattern-matching software on a computer, which thus generates a code[1].

2.4 DNA

DNA sampling requires a form of blood, tissue, or other bodily shaped. DNA biometric is invasive at present and still has to be defined as the analysis of DNA takes 15-20 minutes. DNA sampling could not be matched with real-time witch current technology, but later, when technology advances, DNA sampling may become more significant[1].

3. Robustness

3.1 Factors of Robustness

For considering factors of robustness, consider three inputs as sample input (input1), a correct input that matches the sample input(input 2), and a wrong input that does not match the sample input (input 3).

  1. False Accept Rate (FAR):  The probability of a system that claims that the system has a successful match between the input one and input 3.
  2. False Reject Rate (FRR): The probability of a system that claims that the system has an unsuccessful match between input two and input 3.
  3. Relative Operating Characteristics (ROC): A graph is plotted between FRR and FAR this showing the characteristics.
  4. Equal Error Rate (EER): This is the rate when FAR is equal to FRR. ROC helps to show clearly how FAR, and FRR are changed; the lower the EER, the better and accurate a system is[1].
  5. Failure to Enroll Rate (FER):  The percentage of data that fails to input into the system[1].
  6. Failure to Capture Rate (FTC): The percentage when systems fail to detect biometric characterstics[1].

3.2 Results of Robustness of each authentication

The following were the results of the various biometric authentication using the above parameters.


Type of Biometric















Varied light, indoor/ outdoor


Iris Scan





Indoor environment


Retina Scan





Indoor environment


Palm Vein Recognition





During six months period


Ear Recognition





Only in crime investigation


Voice Recognition





Text-dependent and multilingual


Facial Recognition





Varied light, indoor/ outdoor







Performance not good in tablets







Systems are not accurate

Table 1: Results of the robustness of each authentication

Figure 8: Graph plotted in Excel spreadsheet of the data from the table

  1. Fingerprints: The fingerprint could not detect the impression correctly due to the moisture between the finger and sensor.
  2. Iris Scan: The false analogy of the iris is virtually impossible because of its distinct properties. The iris is closely associated with the human brain and is said to be one of the first parts to disintegrate after death.
  3. Retina Scan: The main drawback of the retina scan is its impulsiveness. The method of obtaining a retina scan is personally nosy. Laser light must be conducted through the cornea of the edge. Also, the transaction of a retina scanner is not secure. An adept operator is required, and the person being scanned has to follow his/her direction.
  4. Palm Vein Recognition: Its position to be used is that the hand must be placed accurately, governed marking have been incorporated, and units are seated so that they are at a comfortable height for most of us.
  5. Ear Recognition: This method has not achieved an exceptional level of security yet. It is simple, and recognizable features of the ear cannot provide a strong establishment of individual identity.
  6. Voice Recognition: Even though this method does not require any specialized or lavish hardware and can be used via a phone line, but the background noises cause a significant problem that shrinks its accuracy.
  7. Facial Recognition: The accuracy of this method is expanding with technology, but it is yet not very astonishing. The current software does not find the face as 'face' at an appropriate place, which can make the result worse. The problems with this technology can create problems when there are distinct twins or any significant changes in hair or beard style.
  8. Signatures: A person does not make a signature persistently the same way. So, the data achieved from the signature of a person has to allow for quite some variability. Most of the signature dynamics pattern verifies the dynamic only. They do not wage consideration to the resulting signature.
  9. DNA: The environment and management can affect measurements. The systems are not precise and require integration or further hardware, and also they cannot be rest once compromised.

4. Future Scope

Following are the approaches by which we can resolve the issues of these biometric authentications:

  1. Fingerprints: A fingernail plate can be used, which segregates features on the surface of the fingernail plate with more precision.
  2. Iris Scan: Various papers have been suggested with more developments on the veracity of iris scanning for the authentication mode in which a three-dimensional camera is primarily preferred for this principle.
  3. Retina Scan: We can use a steep resolution sensor for capturing more precise images of blood vessel samples.
  4. Palm Vein Recognition: We can facilitate the sensor device in order to reduce the overall cost of feature eradication of an individual’s palm vein.
  5. Ear Recognition: We can put some extra effort into pattern recognition in order to increase its complexity.
  6. Voice Recognition: If we develop an excellent combination of artificial intelligence and current voice recognition, it will be a massive profit for biometrics.
  7. Facial Recognition: We can use a three-dimensional camera for data collection. We can also use more precise sensors to capture images of face skin, which looks for the peculiar features in a user's face skin such as visual spots, lines, or birthmarks.
  8. Signatures: If we combine current digital signatures with other methods of verification, signatures, too, will have more potential to cut down fraud and identify fraud by adding more layers of security to the biometric.
  9. DNA: At the moment, time taken to perform a DNA test is usually 15-20 minutes. If we try to integrate the DNA analyzer and combine it with other methods of biometrics, it will become a very secure way for authentication.

5. Conclusion

Biometric Authentication has an excellent scope for private, public, and government agencies. Although the reality is that biometrics is the future of the security industry and it is quickly becoming more recognized as the most accurate identification in today's world. However, it is easy to beat the current generation of biometrics if they are used solely. However, if we combine biometrics with new technology or combine different biometrics, it will be advantageous to add/increase the accuracy of the current generation of biometrics. Biometrics products will become more flexible and capable of serving different purposes, thus accomplishing more than just authentication.


  1. Debnath Bhattacharyya, Rahul Ranjan, Farkhod Alisherov A., and Minkyu Choi  Computer Science and Engineering Department Heritage Institute of Technology, Kolkata-700107, India
  2. Jain, A. K.; Ross, A. & Pankanti, S., "Biometrics: A Tool for Information Security," IEEE Transactions on Information Forensics And Security, Volume 1, issue 2, Jun. 2006
  3. R. Sanchez-Reillo, C. Sanchez-Avila, and A. Gonzales-Marcos, “Biometric identification through hand geometry measurements," IEEE Trans. Pattern Anal. Mach. Intell
  4. C. Marin˜ o Æ M. G. Penedo Æ M. Penas Æ M. J. Carreira F. Gonzalez, "Personal authentication using digital retinal images," Journal of Pattern Analysis and Application, Springer, Volume 9, Issue 1, May. 2006,
  5. J. L. Wayman, "Fundamentals of Biometric Authentication Technologies," International Journal of Image and Graphics, World Scientific Publication
  6. J. Daugman, "The importance of being random: statistical principles of iris recognition," Journal of Pattern Recognition, Elsevier
  7. F. Cardinaux, C. Sanderson, and S. Bengio, "User Authentication via Adapted Statistical Models of Face Images," IEEE Transaction on Signal Processing
  8. Ye Wang; Rane, S.; Draper, S.C.; Ishwar, P., "A Theoretical Analysis of Authentication,  Privacy,  and  Reusability  Across  Secure  Biometric Systems," in Information Forensics and Security,  IEEE Transactions

For images

  1. Figure 1: www.sciencemag.org
  2. Figure 2: www.computerworld.com
  3. Figure 3: www.shuttershock.com
  4. Figure 4: www.matratech.com
  5. Figure 5: www.scienceabc.com
  6. Figure 6: www.aware.com
  7. Figure 7: www.securityindustry.org


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