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

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

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

On robust estimation of the location parameter

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Efficient prediction of COVID-19 cases could prepare the healthcare system to accommodate the COVID-19 cases in the forthcoming days and improve the overall resource management. A hybrid model comprised of an autoregressive filter, a graph convolutional neural network (GCN), and a long short-term memory neural network is proposed for COVID-19 cases prediction in USA. It captures accurately both linearities and nonlinearities present in the time series. An adjacency matrix is exploited in GCN that relies on Granger causality tests applied to historical COVID-19 cases for each state in USA. By doing so, the latent information about the spread of the virus is captured efficiently and the prediction performance of the hybrid model is improved, revealing which state truly affects the other ones. The proposed method outperforms the state-of-the-art techniques.

An Autoregressive Graph Convolutional Long Short-Term Memory Hybrid Neural Network for Accurate Prediction of COVID-19 Cases

Electric Network Frequency (ENF) fluctuations constitute a powerful tool in multimedia forensics. An efficient approach for ENF estimation is introduced with temporal windowing based on the filter-bank Capon spectral estimator. A type of Gohberg-Semencul factorization of the model covariance matrix is used due to the Toeplitz structure of the covariance matrix. Moreover, this approach uses, for the first time in the field of ENF, a temporal window, not necessarily the rectangular one, at the stage preceding spectral estimation. Krylov matrices are employed for fast implementation of matrix inversions. The proposed approach outperforms the state-of-the-art methods in ENF estimation, when a short time window of $1$ second is employed in power recordings. In speech recordings, the proposed approach yields highly accurate results with respect to both time complexity and accuracy. Moreover, the impact of different temporal windows is studied. The results show that even the most trivial methods for ENF estimation, such as the Short-Time Fourier Transform, can provide better results than the most recent state-of-the-art methods, when a temporal window is employed. The correlation coefficient is used to measure the ENF estimation accuracy.

Efficient Capon-Based Approach Exploiting Temporal Windowing For Electric Network Frequency Estimation

This work presents a method for extracting facial information encoded in MPEG-4 format from an animated face model in Maya. The method generates the appropriate data, as specified in the MPEG-4 standard, such as the face definition parameters (FDPs), the face animation parameters (FAPs) and the facial animation table (FAT). The described procedure was implemented as a plug-in for Maya, which requires as inputs only the animated face model and the correspondence between its vertices and the FDPs. The extracted data were tested on a publicly available FAP-player in order to demonstrate the faithful reproduction of the face animation originally created with the high-end 3D graphics modeller. The capabilities of the publicly available FAP player have also been augmented in order to enable loading any proprietary face model and its corresponding FAT.

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MPEG-4 compliant reproduction of face animation created in Maya

Gender information improves emotion classification accura cy. Accordingly, gender classification for emotional speech is an interesting research t opic. A pool of 1418 features is created, 619 of which are tested for the first time in gender cl assification. This is the largest feature set to the best of the authors’ knowledge. The fe atures are related to statistics of pitch, formant, and energy contours, as well as autocorrelation, MPEG-7 descriptors, Fujisaki’s model parameters, jitter, and shimmer. A featur e selection algorithm derives 15 features, which are input to a nearest neighbor classifier, radial-basis function neural network, probabilistic neural network, support vector machines, discriminant analysis-based classifier, classification tree, self organizing map, and ne ural gas network. Two databases are employed: the Berlin database of Emotional Speech and the Danish Emotional Speech database. A perfect classification accuracy is obtained. A s ystematic comparative study of the performance gains among the classifiers and the variants of each particular classifier is undertaken. Furthermore, the databases are assessed concerning gender classification accuracy. Results advance the state-of-the-art.

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Gender classification by processing emotional speech

This paper deals with N-dimensional patterns that are represented as points on the (N − 1)-dimensional simplex. The elements of such patterns could be the posterior class probabilities for N classes, given a feature vector derived by the Bayes classifier for example. Such patterns form N clusters on the (N − 1)-dimensional simplex. We are interested in reducing the number of clusters to N − 1 in order to redistribute the features assigned to a particular class in the N − 1 simplex over the remaining N − 1 classes in an optimal manner by using a self-organizing map. An application of the proposed solution to the re-assignment of emotional speech features classified as neutral into the emotional states of anger, happiness, surprise, and sadness on the Danish Emotional Speech database is presented.

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Constantine Kotropoulos

Papers

An Autoregressive Graph Convolutional Long Short-Term Memory Hybrid Neural Network for Accurate Prediction of COVID-19 Cases

Efficient Capon-Based Approach Exploiting Temporal Windowing For Electric Network Frequency Estimation

MPEG-4 compliant reproduction of face animation created in Maya

Gender classification by processing emotional speech

Self Organizing Maps for Reducing the Number of Clusters by One on Simplex Subspaces