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.

Frontal view recognition in multiview video sequences

Abstract: In this paper, a novel method is proposed as a solution to the problem of frontal view recognition from multiview image sequences. Our aim is to correctly identify the view that corresponds to the camera placed in front of a person, or the camera whose view is closer to a frontal one. By doing so, frontal face images of the person can be acquired, in order to be used in face or facial expression recognition techniques that require frontal faces to achieve a satisfactory result. The proposed method firstly employs the Discriminant Non-Negative Matrix Factorization (DNMF) algorithm on the input images acquired from every camera. The output of the algorithm is then used as an input to a Support Vector Machines (SVMs) system that classifies the head poses acquired from the cameras to two classes that correspond to the frontal or non frontal pose. Experiments conducted on the IDIAP database demonstrate that the proposed method achieves an accuracy of 98.6% in frontal view recognition.

A review of approximate methods for kernel-based big media data analysis

Abstract: With the increasing size of today's image and video data sets, standard pattern recognition approaches, like kernel based learning, need to face new challenges. Kernel-based methods require the storage and manipulation of the kernel matrix, having dimensions equal to the number of training samples. When the data set cardinality becomes large, the application of kernel methods becomes intractable. Approximate kernel-based learning approaches have been proposed in order to reduce the time and space complexities of kernel methods, while achieving satisfactory performance. In this paper, we provide a overview of such approximate kernel-based learning approaches finding application in media data analysis.

A framework for dialogue detection in movies

Abstract: In this paper, we investigate a novel framework for dialogue detection that is based on indicator functions. An indicator function defines that a particular actor is present at each time instant. Two dialogue detection rules are developed and assessed. The first rule relies on the value of the cross-correlation function at zero time lag that is compared to a threshold. The second rule is based on the cross-power in a particular frequency band that is also compared to a threshold. Experiments are carried out in order to validate the feasibility of the aforementioned dialogue detection rules by using ground-truth indicator functions determined by human observers from six different movies. A total of 25 dialogue scenes and another 8 non-dialogue scenes are employed. The probabilities of false alarm and detection are estimated by cross-validation, where 70% of the available scenes are used to learn the thresholds employed in the dialogue detection rules and the remaining 30% of the scenes are used for testing. An almost perfect dialogue detection is reported for every distinct threshold.

A multiple-UAV architecture for autonomous media production

Abstract: Cinematography with Unmanned Aerial Vehicles (UAVs) is an emerging technology promising to revolutionize media production. On the one hand, manually controlled drones already provide advantages, such as flexible shot setup, opportunities for novel shot types and access to difficult-to-reach spaces and/or viewpoints. Moreover, little additional ground infrastructure is required. On the other hand, enhanced UAV cognitive autonomy would allow both easier cinematography planning (from the Director’s perspective) and safer execution of that plan during actual filming; while integrating multiple UAVs can additionally augment the cinematic potential. In this paper, a novel multiple-UAV software/hardware architecture for media production in outdoor settings is proposed. The architecture encompasses mission planning and control under safety constraints, enhanced cognitive autonomy through visual analysis, human-computer interfaces and communication infrastructure for platform scalability with Quality-of-Service provisions. Finally, the architecture is demonstrated via a relevant subjective study on the adequacy of UAV and camera parameters for different cinematography shot types, as well as with field experiments where multiple UAVs film outdoor sports events.

Facial image clustering in stereoscopic videos using double spectral analysis

Abstract: In this work, we are focusing on facial image clustering techniques applied on stereoscopic videos. We introduce a novel spectral clustering algorithm which combines two well-known algorithms: normalized cuts and spectral clustering. Furthermore, we introduce two approach for evaluating the similarities between facial images, one based on Mutual Information and other based on Local Binary Patterns, combined with facial fiducial points and an image registration procedure. Ways of exploring the extra information available in stereoscopic videos are also introduced. The proposed approaches are successfully tested on three stereoscopic feature films and compared against the state-of-the-art. Author-HighlightsWe developed a facial image clustering algorithm for stereoscopic videos.A double spectral analysis was used for performing the clustering.Features that were used included both global (Mutual Information based) and local (Local Binary Patterns).Facial image trajectory information was also used in clustering.Best results occurred for local features and multiple representative images per facial image trajectory.

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.