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 …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

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.

Face recognition: A literature survey

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

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.

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Detecting faces in images: a survey

Usage of digital media has witnessed a tremendous growth during the last decades, as a result of their notable benefits in efficient storage, ease of manipulation and transmission. However these features make digital media vulnerable to copyright infringement, tampering and unauthorized distribution. In the last five years the protection of digital information has received significant attention within the digital media community, and a number of techniques that try to address the problem by hiding appropriate information (e.g. copyright or authentication data) within digital media have been proposed. In this paper we review data hiding techniques for copyright protection of still images and describe some recent research results in this field.

Digital image watermarking: an overview

The methods introduced so far regarding discriminant non-negative matrix factorization (DNMF) do not guarantee convergence to a stationary limit point. In order to remedy this limitation, a novel DNMF method is presented that uses projected gradients. The proposed algorithm employs some extra modifications that make the method more suitable for classification tasks. The usefulness of the proposed technique to frontal face verification and facial expression recognition problems is demonstrated.

A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems

Two hybrid systems for classifying seven categories of human facial expression are proposed The first system combines independent component analysis (ICA) and support vector machines (SVMs) The original face image database is decomposed into linear combinations of several basis images, where the corresponding coefficients of these combinations are fed up into SVMs instead of an original feature vector comprised of grayscale image pixel values The classification accuracy of this system is compared against that of baseline techniques that combine ICA with either two-class cosine similarity classifiers or two-class maximum correlation classifiers, when we classify facial expressions into these seven classes We found that, ICA decomposition combined with SVMs outperforms the aforementioned baseline classifiers The second system proposed operates in two steps: first, a set of Gabor wavelets (GWs) is applied to the original face image database and, second, the new features obtained are classified by using either SVMs or cosine similarity classifiers or maximum correlation classifier The best facial expression recognition rate is achieved when Gabor wavelets are combined with SVMs

ICA and Gabor representation for facial expression recognition

Various approaches have been proposed for simultaneous optical flow estimation and segmentation in image sequences. In this study, the moving scene is decomposed into different regions with respect to their motion, by means of a pattern recognition scheme. The inputs of the proposed scheme are the feature vectors representing still image and motion information. Each class corresponds to a moving object. The classifier employed is the median radial basis function (MRBF) neural network. An error criterion function derived from the probability estimation theory and expressed as a function of the moving scene model is used as the cost function. Each basis function is activated by a certain image region. Marginal median and median of the absolute deviations from the median (MAD) estimators are employed for estimating the basis function parameters. The image regions associated with the basis functions are merged by the output units in order to identify moving objects.

Optical flow estimation and moving object segmentation based on median radial basis function network

In this paper, we propose a novel method aiming at view-independent human action recognition. Action description is based on local shape and motion information appearing at spatiotemporal locations of interest in a video. Action representation involves fuzzy vector quantization, while action classification is performed by a feedforward neural network. A novel classification algorithm, called minimum class variance extreme learning machine, is proposed in order to enhance the action classification performance. The proposed method can successfully operate in situations that may appear in real application scenarios, since it does not set any assumption concerning the visual scene background and the camera view angle. Experimental results on five publicly available databases, aiming at different application scenarios, denote the effectiveness of both the adopted action recognition approach and the proposed minimum class variance extreme learning machine algorithm.

Ioannis Pitas

Papers

Digital image watermarking: an overview

A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems

ICA and Gabor representation for facial expression recognition

Optical flow estimation and moving object segmentation based on median radial basis function network

Minimum Class Variance Extreme Learning Machine for Human Action Recognition