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.

Quality preserving face de-identification against deep cnns

Abstract: In this paper, two face de-identification methods are proposed regarding face identification hindering against a deep neural network. Our work focuses on achieving a delicate balance, so that the facial images are miss-classified by the deep network, while the human observer can still identify the persons depicted in a scene. The proposed methods are based on achieving face de-identification by partly degrading image quality in order to hinder face recognition from deep neural networks, while maintaining the highest possible image quality, at the same time. To this end, we employ de-identification methods based on singular value decomposition and image hypersphere projections, respectively. From the conducted experiments, it can be concluded that these methods are capable of reducing correct face identification rates of the VGG-face network by over 90 %. Moreover, it is shown that these error rates preserve adequate image quality as is demonstrated through the values of the complex wavelet structural similarity index, allowing face recognition by humans contrary to most face de-identification methods.

Object tracking based on Multiscale Morphological Templates

Abstract: This paper presents a novel template representation that can be used in template-based object tracking methods. More specifically, the Multiscale Morphological Template is introduced and incorporated in a template-based object tracking algorithm. The proposed template can be updated over time to cope with changing environment/object conditions. The algorithm is applied to face tracking in scenes with complex background. Results of the object tracking algorithm using the proposed and existing template representations are compared using measures based on ground truth data. The proposed template is proved to be superior to existing templates.

Graph representations using adjacency matrix transforms for clustering

Abstract: This paper is meant as a proof of concept regarding the application of standard 2D signal representation and feature extraction tools that have wide use in their respective fields to graph related pattern recognition tasks such as, in this case, clustering. By viewing the adjacency matrix of a graph as a 2-dimensional signal, we can apply 2D Discrete Cosine Transform (DCT) to it and use the relation between the adjacency matrix and the values of the DCT bases in order to cluster nodes into strongly connected components. By viewing the adjacency matrices of multiple graphs as feature vectors, we can apply Principal Components Analysis (PCA) to decorrelate them and achieve better clustering performance. Experimental results on synthetic data indicate that there is potential in the use of such techniques to graph analysis.

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.