A fuzzy interval valued fusion technique for multi-modal 3D face recognition

Ramalingam, Soodamani; Maheswari, Uma

doi:10.1109/ccst.2016.7815709

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Methods of fuzzy logic [31] are in many cases more in demand. However, additional input on fuzzy data membership functions is needed to use them effectively.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Рубан

Horenskyi

Romanenkov

et al. 2022

EEJET

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The problem of improving the methodological and algorithmic support of the complexing process by developing models of adaptive redundant complexing of weighted interval data has been solved. The object of this study is the process of complexing interval data obtained from several independent sources; the subject is the algebraic methods of excessive complexing of weighted interval data. The relevance of the task is due to the severity of the problem of consolidating homogeneous data in order to obtain more accurate and relevant information about the object or process under study. Models have been developed that, unlike the existing ones, make it possible a posteriori to take into account the accuracy of experts at the preliminary stage of expert evaluation. A single analytical form of the model for processing weighted interval and point estimates with the possibility of structural and parametric tuning is proposed. It allows one to increase the degree of automation of processing expert assessments under conditions of interval uncertainty. Recommendations for the practical application of the proposed models have been formulated. Options for parametric configuration of preference functions were indicated depending on the characteristics of weighted interval estimates. The commonality of the limiting cases of the proposed models with previously known ones is proved. The example shows the shift of the integrated assessment to the side of more accurate assessments at the previous stage of source assessment. The adaptability of the proposed models is illustrated. At the same time, a slight, on average, about 10 %, expansion of the complexed interval relative to the primary ones was registered. The built models and algorithms can be used in automated expert systems, as well as in cascade models of information processing and compression.

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“…Methods of fuzzy logic [31] are in many cases more in demand. However, additional input on fuzzy data membership functions is needed to use them effectively.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Рубан

Horenskyi

Romanenkov

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…Axiomatic fuzzy set was utilized to obtain a semantic facial descriptor in [17]. Interval value fuzzy TOPSIS technique was applied to 3D facial classification system in [18]. Recently, the results of AHP by experts were applied to a neural network classifier [19].…”

Section: Introductionmentioning

confidence: 99%

Linguistic Descriptors in Face Recognition

Karczmarek

Kiersztyn

Pedrycz

et al. 2018

Int. J. Fuzzy Syst.

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In this study, we propose linguistic descriptorsbased approach to the problem of face identification realized by both humans and computers. This approach is motivated by an evident observation that linguistic descriptors offer an ability to formalize and exploit important pieces of knowledge describing human's face. These entities are used by people in face recognition and could be found of importance in building machine-oriented recognition schemes. Moreover, evident humans' abilities to recognize other individuals can be incorporated into computational face recognition problems as an invaluable vehicle improving recognition rate of machine-oriented classifiers. Specifically, we propose an application of analytic hierarchy process to determine linguistic values of facial features. The experts' assessments of faces in terms of such attributes support coping with uncertainty captured through experts' decisions result in a set of useful assuring the desired property of inter-class similarities and betweenclass differences among faces. It is worth noting that the method presented in this study can be easily applied to any other classification problem with the presence of experts.

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A fuzzy interval valued fusion technique for multi-modal 3D face recognition

Cited by 2 publications

References 14 publications

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Linguistic Descriptors in Face Recognition

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