Ioannis Pitas

Bio: 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.

Enhancing class discrimination in Kernel Discriminant Analysis

Abstract: In this paper, we propose an optimization scheme aiming at optimal nonlinear data projection, in terms of Fisher ratio maximization. To this end, we formulate an iterative optimization scheme consisting of two processing steps: optimal data projection calculation and optimal class representation determination. Compared to the standard approach employing the class mean vectors for class representation, the proposed optimization scheme increases class discrimination in the reduced-dimensionality feature space. We evaluate the proposed method in standard classification problems, as well as on the classification of human actions and face, and show that it is able to achieve better generalization performance, when compared to the standard approach.

Learning Multi-Graph Regularization for SVM Classification

Abstract: A classification method that emphasizes on learning the hyperplane that separates the training data with the maximum margin in a regularized space, is presented In the proposed method, this regularized space is derived by exploiting multiple graph structures, in the SVM optimization process Each of the employed graph structure carries some information concerning a geometric or semantic property about the training data, eg, local neighborhood area and global geometric data relationships The proposed method introduces information from each graph type to the standard SVM objective, as a projection of the SVM hyperplane to such a direction, where a specific property of the training data is highlighted We show that each data property can be encoded in a regularized kernel matrix Finally, response in the optimal classification space can be obtained by exploiting a weighted combination of multiple regularized kernel matrices Experimental results in face recognition and object classification denote the effectiveness of the proposed method

A robust feature-based technique for watermarking frontal face images.

Abstract: We introduce a novel method for embedding and detecting a chaotic watermark in the digital spatial domain of color face images, based on localizing salient facial features. Simulation results prove the robustness of the method to several kinds of attack, such as compression, filtering, scaling, cropping and rotation.

Algorithms and structures for convolutions over Galois fields

Abstract: The computation of cyclic convolutions in Galois fields is an integral part of coding theory and formulation as well as of many signal processing applications. In this paper, we introduce a method for the computation of such convolutions that minimizes, in theory, the computational complexity of the algorithm. We also propose special-purpose computer architecture schemes for the efficient realization of the algorithm, and in general, for efficient calculation of convolutions in Galois fields.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Face recognition: A literature survey

Abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Detecting faces in images: a survey

Abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.