Direct and Indirect Human Computer Interaction Based Biometrics

Yampolskiy, Roman V.; Govindaraju, Venu

doi:10.4304/jcp.2.10.76-88

Cited by 12 publications

(8 citation statements)

References 59 publications

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“…The popularity of some movies is expected to follow seasonal patterns. User-side effects include changing tastes and preferences of customers, some of which may be short-term or contextual and therefore likely reoccurring (mood, activity, company, etc), changing [75,62] perception of rating scale, possible change of rater within household and alike problems. As suggested in [45] popular windowing and instance weighing approaches for handling concept drift are not the best choice simply because in collaborative filtering the relations between ratings is of the main importance for predictive modeling.…”

Section: Managementmentioning

confidence: 99%

An Overview of Concept Drift Applications

Žliobaitė

Pechenizkiy

Gama

2015

Studies in Big Data

261

167

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In most challenging data analysis applications, data evolve over time and must be analyzed in near real time. Patterns and relations in such data often evolve over time, thus, models built for analyzing such data quickly become obsolete over time. In machine learning and data mining this phenomenon is referred to as concept drift. The objective is to deploy models that would diagnose themselves and adapt to changing data over time. This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks. First we overview and categorize application tasks for which the problem of concept drift is particularly relevant. Then we construct a reference framework for positioning application tasks within a spectrum of problems related to concept drift. Finally, we discuss some promising research directions from the application perspective, and present recommendations for application driven concept drift research and development.

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Section: Managementmentioning

confidence: 99%

An Overview of Concept Drift Applications

Žliobaitė

Pechenizkiy

Gama

2015

Studies in Big Data

261

167

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“… Behavioral Biometrics -Quantifies behavioral traits exhibited by users and uses resulting feature profiles to verify identity [93]. Examples of analyzed artifacts may include text, art, as well as direct or indirect human-computer-interaction [94].  CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) -obtains input from an agent and classifies producing agent as human or artificial [95,96].…”

Section: Designometry -Generalization Of the Proposed Analysismentioning

confidence: 99%

On the origin of synthetic life: attribution of output to a particular algorithm

Yampolskiy¹

2016

Phys. Scr.

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With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to be able to distinguish between natural and artificial life forms. In this paper, we present this challenge as a generalized version of Darwin's original problem, which he so brilliantly addressed in On the Origin of Species. After formalizing the problem of determining origin of samples we demonstrate that the problem is in fact unsolvable, in the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal. Our results should be of interest to astrobiologists and scientists interested in producing a more complete theory of life, as well as to AI-Safety researchers.

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“…A limited number of studies try to investigate the use of biometric technology in Human Computer Interaction [6]. The majority of them look into the problem from the biometric perspective; the aim is either to build appropriate interfaces to increase the adoption of biometrics for security reasons [4,8] or to create authentication systems based on users' interaction with computers in their everyday work [24]. The use of biometrics for creating user-adapted human computer interfaces only recently gained some attention with the creation of special tracks in some conferences [10].…”

Section: Literature Reviewmentioning

confidence: 99%

On the Quantification of Aging Effects on Biometric Features

Lanitis

Tsapatsoulis

2010

IFIP Advances in Information and Communication Technology

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Biometric templates are often used in intelligent human computer interaction systems that include automated access control and personalization of user interaction. The effectiveness of biometric systems is directly linked with aging that causes modifications on biometric features. For example the long term performance of person identification systems decreases as biometric templates derived from aged subjects may display substantial differences when compared to reference templates whereas in age estimation, aging variation allows the age of a subject to be estimated. In this paper we attempt to quantify the effects of aging for different biometric modalities facilitating in that way the design of systems that use biometric features. In this context the homogeneity of statistical distributions of biometric features belonging to certain age classes is quantified enabling in that way the definition of age sensitive and age invariant biometric features. Experimental results demonstrate the applicability of the method in quantifying aging effects.

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Direct and Indirect Human Computer Interaction Based Biometrics

Cited by 12 publications

References 59 publications

An Overview of Concept Drift Applications

An Overview of Concept Drift Applications

On the origin of synthetic life: attribution of output to a particular algorithm

On the Quantification of Aging Effects on Biometric Features

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