CHALLENGES IN BIOMETRICS SYSTEMS

CHALLENGES IN BIOMETRICS SYSTEMS

Though biometric systems have been successfully deployed in a number of real-world apps, biometrics is not so far an entirely resolved problem. The three main factors that contribute to the complexity of biometric system design are accuracy (FAR, GAR and rank-1 identification rate), scalability (size of the database) and usability (easiness of usage, security as well as given privacy). The author Anil Jain et al. state that the grand challenge in biometrics is to design a system that operates in the extremes of all these three factors. In other words, the challenge is to develop a biometric system that is highly exact as well as also secure, appropriate to utilize plus straightforwardly scalable towards a large population. We now discuss the major obstacles that hinder the design of such an “ideal” biometric system [64].

Accuracy

An ideal biometric system should always provide the correct identity decision when a biometric sample is offered. Nevertheless, a biometric structure not often come across a sample of a user’s biometric trait that is exactly the same as the template. The main factors affecting the accuracy of a biometric system are:

  • Noisy sensor data: Noise can be present in the acquired biometric data mainly due to defective or improperly maintained sensors. For example, accumulation of dirt or the residual remains on a fingerprint sensor can result in a noisy fingerprint image or Failure to focus the camera appropriately can lead to blurring in face and iris images. The recognition accuracy of a biometric system is highly sensitive to the quality of the biometric input and noisy data can result in a significant reduction in the GAR of a biometric system.
  • Non-universality: If every individual in the target population is able to present the biometric trait for acknowledgement of an individual, at that time the trait is assumed to be present as universal. Universality is one of the basic requirements for a biometric identifier. However, not all biometric traits are actually universal. The organization of National Institute of Standards and Technology (NIST) has reported that it is not possible to obtain a good quality fingerprint from approximately two percent of the population (people with hand-related incapacities, physical labors having several cuts as well as bruises on their fingertips, in addition to human being having very oily or else dry fingers). Later, such kind of persons can probably not be able to get enroll in a fingerprint authentication system. Correspondingly, peoples with long eye-lashes and those suffering from eye abnormalities or diseases similar to nystagmus, cataract, glaucoma, as well as aniridia, could possibly be not able to provide good quality iris images for automatic recognition.
  • Inter-user similarity: Inter-user similarity refers to the overlap of the biometric samples from two different individuals in the feature space. The lack of uniqueness in the biometric feature set restricts the discriminative ability of the biometric system. In the case of a biometric identification system, the inherent information constraint in the feature set results in an upper bound on the number of unique individuals that can be accommodated.
  • Lack of invariant representation: Biometric samples of an individual usually exhibit large intra-user variations. The variations may be due to improper interaction of the user with the sensor (e.g., changes due to rotation, translation and applied pressure when the user places his finger on a fingerprint sensor, changes in pose and expression when the user stands in the presence of a camera, and so on.), utilize of several dissimilar sensors for the duration of registration as well as verification, alterations in the ambient ecological circumstances (e.g., illumination changes in a face recognition system) and inherent changes in the biometric trait (e.g., appearance of wrinkles due to aging or presence of facial hair in face pictures, existence of wounds in a finger-impression, and so on). Ideally, the features extracted from the biometric data must be relatively invariant to these changes. However, in most practical biometric systems the features are not invariant and therefore complex matching algorithms are required to take these variations into account.

Due to the above factors, the error rates associated with biometric systems are higher than what is required in many applications.

Scalability

In the case of a biometric verification system, the size of the database (number of enrolled users in the system) is not an issue because each authentication attempt basically involves matching the query with a single template. In the case of large scale identification systems where N identities are enrolled in the system, sequentially comparing the query with all the N templates is not an effective solution due to two reasons. Firstly, the throughput2 of the system would be greatly reduced if the value of N is quite large. For example, if the size of the database is 1 million and if each match requires an average of 100 microseconds, then the throughput of the system will be less than 1 per minute. Furthermore, the large number of identities also affects the false match rate of the system adversely. Hence, there is a need for efficiently scaling the system. This is usually achieved by a process known as filtering or indexing where the database is pruned based on extrinsic (e.g., gender, ethnicity, age, etc.) or intrinsic (e.g., fingerprint pattern class) factors and the search is restricted to a smaller fraction of the database that is likely to contain the true identity of the user. There are very few published studies on efficiently indexing biometric databases and this is still an active area of research in the biometrics community.

Security and Privacy

Although it is difficult to steal someone’s biometric traits, it is still possible for an impostor to circumvent a biometric system in a number of ways. For example, it is possible to construct fake or spoof fingers using lifted fingerprint impressions (e.g., from the sensor surface) and utilize them to circumvent a fingerprint recognition system. Behavioral traits like signature and voice are more susceptible to such attacks than anatomical traits.

The most straightforward way to secure a biometric system is to put all the system modules and the interfaces between them on a smart card (or more generally a protected processor). In such kind of systems, that acknowledged as match-on-card or system-on-card technology, sensor, feature extractor, matcher and template reside on the card. The advantage of this technology is that the user’s biometric data never leaves the card which is in the user’s possession. However, system-on-card solutions are not appropriate for most large-scale verification applications because they are still expensive and users must carry the card with them at all times. Moreover, system-on-card solutions cannot be used in identification applications.

One of the critical issues in biometric systems is protecting the template of a user which is typically stored in a database or a smart ID card. Appropriated biometric templates could also be utilized to compromise the security of the system in the following two ways.

  • The stolen template can be replayed to the matcher to gain unlicensed entree, and also
  • A physical spoof can be created from the template to gain unauthorized access to the system (as well as other systems which use the same biometric trait).

Note that an adversary can covertly acquire the biometric information of a genuine user (e.g., lift the fingerprint from a surface touched through the client). Therefore, spoof attacks are conceivable even after the adversary does not have access to the biometric template. However, the adversary needs to be in the physical proximity of the person he is attempting to impersonate in order to covertly acquire his biometric trait. On the other hand, even a remote adversary can create a physical spoof if he gets access to the biometric template information.

Unlike passwords, when biometric templates are compromised, it is not possible for a legitimate user to revoke his biometric identifiers and switch to another set of uncompromised identifiers. Because of this irrevocable characteristics of biometric information, an attack in contradiction of the stowed templates creates a major security and privacy threat in a biometric system.

Since a biometric trait is a permanent link between a person and his/her ID uniqueness, it might probably be easily liable to exploitation in such a way that a person’s right to privacy and anonymity is compromised. A common type of abuse of biometric identifiers is function creep where the acquired biometric identifiers are later used for purposes other than the intended purpose. For example, Disney World in Orlando collects fingerprints from park visitors in order to prevent customers from sharing the tickets with others. However, it is possible that the same fingerprints may be used later for searching against a criminal fingerprint database or cross-link it to a person’s health records. Hence, strategies to prevent function creep and to ensure an individual’s privacy are urgently needed.

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