Applications, Benefits And Challenges Of Cognitive Biometric Technologies: [Essay Example], 759 words GradesFixer

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Applications, Benefits and Challenges of Cognitive Biometric Technologies

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Cognitive biometric technologies have been in demand over the past decade and is growing at a fast rate with the advent of machine learning and artificial intelligence algorithms. Some of the applications are listed below which are in use.

Cognitive based logon process for computing device

A method of user logon to a computing device or computer system that, distinct from requiring entry of a set of known logon credentials such as a username and password, introduces an additional thought-directed user interface whereby the user must respond to one or more prompts that measure the user’s cognitive function at the time of logon or during an active logon session. The user’s responses to these prompts are evaluated for several purposes, including determining whether the user demonstrates the required level of cognitive function to gain access to the computer system or continue an active logon session. The user’s responses and associated data may also be stored and retrieved at a later time for various purposes, including determining whether and to what extent the user’s level of cognitive function is improving, diminishing, or remaining static over time.

BioCatch’s Technology

BioCatch, a cybersecurity company that delivers behavioural biometrics, analysing human-device interactions to protect users and data. It has three main capabilities: identity proofing, continuous validation, fraud prevention. BioCatch compares the user behaviour in real-time against the profile to return an actionable risk score. An add-on module gives fraud teams with visualization of behavioural data and advanced analytics, enabling rule setting, and reliable fraud reporting in real time.

IBM’S Trusteer Pinpoint

Detect uses behavioural biometrics, with artificial intelligence for real time cognitive imposter recognition. This utility of the system implements machine learning algorithms to study how an individual reacts to stimuli. It can understand small mouse movements and variations in syntax, and semantics, while creating user models with each interaction which can be used later so it gets smarter and more efficient with time. It uses fraud detection capacities with a reliable pattern recognition tool to help identify problems and remove evolving threats.

Comparative analysis

Cognitive methods have a lot of advantages with respect to traditional forms of biometrics.

  • Objectivity: By means of objectivity it’s possible to make assessments that do not depend upon the user perception. Objectivity is a necessary property that increases the reliability of a measurement technique.
  • Multidimensionality: Multidimensional measures are able to give out different faces of user state
  • Unobtrusiveness: Although cognitive measures require the placement of electrodes on the body, they don’t directly interfere with user tasks like “secondary task measures”. Therefore, they are considered as unobtrusive measures.
  • Implicitness: By comparing the “primary task measures”, these methods do not require the measurement of overt performance. They provide the necessary information implicitly (covertly).
  • Continuity and Responsiveness: Psychophysiological measures are continuous signals and therefore they can be used in real-time. They allow researchers to examine both short-term and long-term bodily reactions. Thus, observing changes when they occur in response to manipulations of user stimuli is possible.

These also include privacy compliance, robustness against spoofing attacks, intrinsic liveness detection, and universality. On the other hand, this novel method also poses some challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are some of them. Some of them have also been elaborated below:

  • Special Equipment: Cognitive signals are measured by using special equipment and it may be costly according to capabilities of the purchased system. Besides, different electrodes which are attached to body are used in these measurements. The selection and correct placement of the electrodes are crucial to attain noise free data. Therefore, sufficient amount of attention and time should be given to personnel training and device maintenance.
  • Data Acquisition and Interpretation: Psychophysiological measures are mostly weak electrical signals and may be highly susceptible to noise. Therefore, suitable filtering techniques should be selected and applied to data. Instead a number of them are very vulnerable to confounding factors like ambient lightning (pupil dilation), power grid (ERP), and body movement (ECG). These factors should not be omitted; otherwise misleading results will be inevitable. Data interpretation is another obstacle for researcher and engineers. It’s because of that these measures produce large amount of hard-to-analyse data.
  • Unnaturalness: Especially in laboratories, the environment is artificial and unnatural more sufficiently. With advances of new trends in technologies portable and wireless solutions are possible for some measures like EEG, GRS and HR. if not; electrodes are joined to user by cables and this restrains the movement of an individual and break the naturalness of the interaction between the user and the system.

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