dc.contributor.author | Lanitis, A. | en |
dc.contributor.author | Draganova, C. | en |
dc.contributor.author | Christodoulou, Chris C. | en |
dc.creator | Lanitis, A. | en |
dc.creator | Draganova, C. | en |
dc.creator | Christodoulou, Chris C. | en |
dc.date.accessioned | 2019-11-13T10:40:54Z | |
dc.date.available | 2019-11-13T10:40:54Z | |
dc.date.issued | 2004 | |
dc.identifier.issn | 1083-4419 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54356 | |
dc.description.abstract | We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context, we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application, we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier, and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context, we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases, part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans. | en |
dc.source | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0742290014&doi=10.1109%2fTSMCB.2003.817091&partnerID=40&md5=c42352c3f5d7866004106bb645ccfe5a | |
dc.subject | methodology | en |
dc.subject | age | en |
dc.subject | Age Factors | en |
dc.subject | article | en |
dc.subject | Algorithms | en |
dc.subject | Neural networks | en |
dc.subject | human | en |
dc.subject | Humans | en |
dc.subject | algorithm | en |
dc.subject | Aging | en |
dc.subject | clinical trial | en |
dc.subject | Reproducibility of Results | en |
dc.subject | comparative study | en |
dc.subject | histology | en |
dc.subject | physiology | en |
dc.subject | sensitivity and specificity | en |
dc.subject | reproducibility | en |
dc.subject | validation study | en |
dc.subject | anthropometry | en |
dc.subject | Parameter estimation | en |
dc.subject | artificial intelligence | en |
dc.subject | automated pattern recognition | en |
dc.subject | Pattern Recognition, Automated | en |
dc.subject | Functions | en |
dc.subject | Error analysis | en |
dc.subject | Automation | en |
dc.subject | Hierarchical systems | en |
dc.subject | Classification (of information) | en |
dc.subject | artificial neural network | en |
dc.subject | Neural Networks (Computer) | en |
dc.subject | Image analysis | en |
dc.subject | Human computer interaction | en |
dc.subject | forensic medicine | en |
dc.subject | Face recognition | en |
dc.subject | computer assisted diagnosis | en |
dc.subject | Image Interpretation, Computer-Assisted | en |
dc.subject | photography | en |
dc.subject | Automatic indexing | en |
dc.subject | face | en |
dc.subject | Human aging | en |
dc.subject | Image classification | en |
dc.title | Comparing Different Classifiers for Automatic Age Estimation | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/TSMCB.2003.817091 | |
dc.description.volume | 34 | |
dc.description.issue | 1 | |
dc.description.startingpage | 621 | |
dc.description.endingpage | 628 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :311</p> | en |
dc.source.abbreviation | IEEE Trans Syst Man Cybern Part B Cybern | en |
dc.contributor.orcid | Christodoulou, Chris C. [0000-0001-9398-5256] | |
dc.gnosis.orcid | 0000-0001-9398-5256 | |