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.

Facial image clustering in 3D video using constrained Ncut

Abstract: In this paper a novel variant of the Normalized Nut (N-Cut) clustering algorithm that incorporates imposed constraints is implemented and evaluated on facial image clustering for 3D video analysis. The clustering problem is seen as a graph cut problem through a similarity matrix representing the relation among the vertices, i.e. facial images in this work. Mutual Information is used as similarity metric, applied on the HSV color space of the original images. This work considers the incorporation of constraints either regarding similarity or dissimilarity derived from a priori available information in the clustering procedure and evaluates the performance increase by their use. Experiments are conducted on 3D videos where a priori information about the facial images exists.

Problems and Challenges in Multimedia Networking and Content Protection

Abstract: The digital networked environment necessitates the development of protection techniques for multimedia product access and distribution. This paper refers to protection schemes for copyright and content originality of multimedia products through invisible watermarking. In particular, we present the basic protection schemes and fundamental concepts. The elementary partial algorithms, which can consist an overal watermark-ing system, are given and their basic characteristics are studied. EEcient protection demands from watermarks to have special features and obey conditions that should be satissed strictly. Beside watermarking, the importance and the necessity of a proper product registration is stated.

Semi-supervised dimensionality reduction on data with multiple representations for label propagation on facial images

Abstract: In this paper a novel method is introduced for semi-supervised dimensionality reduction on facial images extracted from stereo videos. It operates on image data with multiple representations and calculates a projection matrix that preserves locality information and a priori pairwise information, in the form of must-link and cannot-link constraints between the various data representations, as well as label information for a percentage of the data. The final data representation is a linear combination of the projections of all data representations. The performance of the proposed Semi-supervised Multiple Locality Preserving Projections method was evaluated in person identity label propagation on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods.

Using support vector machines for face authentication based on elastic graph matching

Abstract: A novel method for enhancing the performance of elastic graph matching in face authentication is proposed Our objective is to weigh the local matching errors at the nodes of an elastic graph according to their discriminatory power We propose a novel approach to discriminant analysis that re-formulates Fisher's linear discriminant ratio to a quadratic optimization problem subject to inequality constraints by combining statistical pattern recognition and support vector machines The method is applied to frontal face authentication on the M2VTS database

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.