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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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Proceedings ArticleDOI
Facial feature extraction in frontal views using biometric analogies
Sofia Tsekeridou,Ioannis Pitas +1 more
TL;DR: A face detection and facial feature extraction in frontal views algorithm based on principles described in [1] but extends the work by considering: (a) the mirror-symmetry of the face in the vertical direction and (b) facial biometric analogies depending on the size of the faces estimated by the face localization method.
Journal ArticleDOI
Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification
TL;DR: A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated and Experimental results indicate that the proposed framework greatly improves the face- Verification performance.
Proceedings ArticleDOI
3D Human Action Recognition for Multi-view Camera Systems
TL;DR: This paper presents a novel approach for combining optical flow into enhanced 3D motion vector fields for human action recognition, and compares the performance of the 3D-MC and HMC descriptors, and shows promising experimental results for the publicly available i3DPost Multi View Human Action Dataset.
Proceedings ArticleDOI
A new sparse image representation algorithm applied to facial expression recognition
I. Bociu,Ioannis Pitas +1 more
TL;DR: This paper found that the newly proposed algorithm discriminant non-negative matrix factorization (DNMF) shows superior performance by achieving a higher recognition rate, when compared to NMF and LNMF.
Journal ArticleDOI
Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks
TL;DR: Two approaches for ultrasonic image processing are examined and a modification of the learning vector quantizer (L(2 ) LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L(2) mean instead of the sample arithmetic mean of the input observations.