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.

A perceptual hashing algorithm using latent dirichlet allocation

Abstract: This paper investigates the possibility of extracting latent aspects of a video, using visual information about humans (e.g. actors' faces), in order to develop a fingerprinting (replica detection) framework. We employ a generative probabilistic model, namely Latent Dirichlet Allocation (LDA), so as to capture latent aspects of a video, using facial semantic information derived from the video. We use the bag-of-words concept, (bag-of-faces in our case) in order to ensure exchangeability of the latent variables (e.g. topics). The video topics are modeled as a mixture of distributions of faces in each video. This generative probabilistic model has already been used in the case of text modeling with good results. Experimental results provide evidence that the proposed method performs very efficiently for video fingerprinting.

Improving the detection reliability of correlation-based watermarking techniques

Abstract: The performance of watermarking schemes based on correlation detection is closely related to the frequency characteristics of the watermark sequence. In order to improve both detection reliability and robustness against attacks, embedding of watermarks with high-frequency spectrum, in the low frequencies of the DFT domain, is introduced in this paper and theoretical analysis of correlation based watermarking techniques with multiplicative embedding is performed. The proposed watermarking framework is successfully applied to audio signals, demonstrating its superiority with respect to both robustness and inaudibility. Experiments are conducted, in order to verify the validity of the theoretical analysis results.

Semantic Image Segmentation Guided By Scene Geometry

Abstract: Semantic image segmentation is an important functionality in various applications, such as robotic vision for autonomous cars, drones, etc. Modern Convolutional Neural Networks (CNNs) process input RGB images and predict per-pixel semantic classes. Depth maps have been successfully utilized to increase accuracy over RGB-only input. They can be used as an additional input channel complementing the RGB image, or they may be estimated by an extra neural branch under a multitask training setting. Contrary to these approaches, in this paper we explore a novel regularizer that penalizes differences between semantic and self-supervised depth predictions on presumed object boundaries during CNN training. The proposed method does not resort to multitask training (which may require a more complex CNN backbone to avoid underfitting), does not rely on RGB-D or stereoscopic 3D training data and does not require known or estimated depth maps during inference. Quantitative evaluation on a public scene parsing video dataset for autonomous driving indicates enhanced semantic segmentation accuracy with zero inference runtime overhead.

Frontal facial pose recognition using a discriminant splitting feature extraction procedure

Abstract: Frontal facial pose recognition deals with classifying facial images into two-classes: frontal and non-frontal. Recognition of frontal poses is required as a preprocessing step to face analysis algorithms (e.g. face or facial expression recognition) that can operate only on frontal views. A novel frontal facial pose recognition technique that is based on discriminant image splitting for feature extraction is presented in this paper. Spatially homogeneous and discriminant regions for each facial class are produced. The classical image splitting technique is used in order to determine those regions. Thus, each facial class is characterized by a unique region pattern which consist of homogeneous and discriminant 2-D regions. The mean intensities of these regions are used as features for the classification task. The proposed method has been tested on data from the XM2VTS facial database with very satisfactory results.

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.