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.

Nonlinear location and scale estimators of fuzzy numbers

Abstract: In this correspondence, the extension principle is used in order to fuzzify location and scale estimators when used on fuzzy numbers. First, fuzzy nonlinear means are defined as extensions of the corresponding crisp means. Fuzzy L location and scale estimators, which are based on fuzzy-order statistics, are defined as extensions of the crisp L location and scale estimators. The most widely used scale estimator, which is known as the sample standard deviation, is also extended to fuzzy numbers through the extension principle. Equivalent relations that can be used to calculate the fuzzy estimators by using crisp arithmetic are also given for each one of the proposed fuzzy estimators.

Pseudoautomatic lip contour detection based on edge direction patterns

Abstract: Detection and tracking of the lip contour is an important issue in lipreading. While there are solutions for lip tracking once a good contour initialization in the first frame is available, the problem of finding such a good initialization is not yet solved automatically, but done manually. Solutions based on edge detection and tracking have failed when applied to real world mouth images. In this paper, we propose a solution to lip contour detection that minimizes user interaction by requiring a minimal number of points to be marked manually on the mouth image. The proposed approach is based on edge detection using gradient masks and edge following. The method is based on the examination of gradient direction patterns in the lip area, and makes use of the local direction constancy along the lip contours, as opposed to the other regions of the mouth image that are characterized by random edge directions.

Representative Class Vector Clustering-Based Discriminant Analysis

Abstract: Clustering-based Discriminant Analysis (CDA) is a well-known technique for supervised feature extraction and dimensionality reduction. CDA determines an optimal discriminant subspace for linear data projection based on the assumptions of normal subclass distributions and subclass representation by using the mean subclass vector. However, in several cases, there might be other subclass representative vectors that could be more discriminative, compared to the mean subclass vectors. In this paper we propose an optimization scheme aiming at determining the optimal subclass representation for CDA-based data projection. The proposed optimization scheme has been evaluated on standard classification problems, as well as on two publicly available human action recognition databases providing enhanced class discrimination, compared to the standard CDA approach.

Laplacian one class extreme learning machines for human action recognition

Abstract: A novel OCC method for human action recognition namely the Laplacian One Class Extreme Learning Machines is presented The proposed method exploits local geometric data information within the OC-ELM optimization process It is shown that emphasizing on preserving the local geometry of the data leads to a regularized solution, which models the target class more efficiently than the standard OC-ELM algorithm The proposed method is extended to operate in feature spaces determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions Its superior performance against other OCC options is consistent among five publicly available human action recognition datasets

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.