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Ioannis Pitas
Researcher at Aristotle University of Thessaloniki
Publications - 826
Citations - 26338
Ioannis Pitas is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Facial recognition system & Digital watermarking. The author has an hindex of 76, co-authored 795 publications receiving 24787 citations. Previous affiliations of Ioannis Pitas include University of Bristol & University of York.
Papers
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Journal ArticleDOI
Color-based descriptors for image fingerprinting
TL;DR: The system was evaluated with receiver operating characteristic (ROC) analysis on a large database of 919 original images consisting of randomly drawn art images and similar images from specific categories, along with 30 transformed images for each original, totaling 27570 images.
Journal ArticleDOI
Novel Multiclass Classifiers Based on the Minimization of the Within-Class Variance
TL;DR: A novel class of multiclass classifiers inspired by the optimization of Fisher discriminant ratio and the support vector machine (SVM) formulation is introduced, which is applied to face recognition and other classification problems using Mercer's kernels.
Book ChapterDOI
Facial Feature Extraction and Determination of Pose
TL;DR: A set of methods for the extraction of facial features as well as for the determination of the gaze direction are described to define a sufficient set of feature distances so that a unique description of the structure of a face is produced.
Journal ArticleDOI
A mutual information based face clustering algorithm for movie content analysis
TL;DR: This paper uses the HSV color space of a facial image (more precisely, only the hue and saturation channels) in order to calculate the mutual information similarity matrix of a set of facial images, making full use of the similarity matrix symmetries, so as to lower the computational complexity of the new mutual information calculation.
Proceedings ArticleDOI
Automatic detection of vocal fold paralysis and edema
TL;DR: A combined scheme of linear prediction analysis for feature extraction along with linear projection methods for feature reduction followed by known pattern recognition methods on the purpose of discriminating between normal and pathological voice samples is proposed.