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

Morphological elastic graph matching applied to frontal face authentication under well-controlled and real conditions

The classification of utterances into five basic emotional states is studied. A total of 87 statistical characteristics of pitch, energy, and formants is extracted from 500 utterances of the Danish emotional speech database. An evaluation of the classification capability of each feature is performed with respect to the probability of correct classification achieved by the Bayes classifier that models the feature probability density function as a mixture of Gaussian densities. Next, the feature subset that yields the highest probability of correct classification is found using the sequential floating forward selection algorithm. The probability of correct classification is estimated via cross-validation and the probability density functions are modelled as mixtures of 2 or 3 Gaussian densities. The results demonstrate that the Bayes classifier which employs mixtures of 2 Gaussian densities can achieve a probability of correct classification equal to 0.55, whereas the human classification score is 0.67 for the database considered and random classification would give a probability of correct classification equal to 0.20.

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Emotional speech classification using Gaussian mixture models

In this paper, we elaborate on mobile phone identification from recorded speech signals. The goal is to extract intrinsic traces related to the mobile phone used to record a speech signal. Mel frequency cepstral coefficients (MFCCs) are extracted from any recorded speech signal at a frame level. The sequences of the MFCC vectors extracted from each recording device train a Gaussian Mixture Model with diagonal covariance matrices. A Gaussian supervector is derived by concatenating the mean vectors and the main diagonals of the covariance matrices that is used as a template for each device. Experiments were conducted on a database of 21 mobile phones of various models from 7 different brands. The aforementioned database, that is called MOBIPHONE, was collected by recording 10 utterances, uttered by 12 male speakers and another 12 female speakers, randomly chosen from the TIMIT database. Three commonly used classifiers were employed, such as Support Vector Machines with different kernels, a Radial Basis Functions neural network, and a Multi-Layer Perceptron. The best identification accuracy (97.6%) was obtained by the Radial Basis Functions neural network.

Mobile phone identification using recorded speech signals

From the Publisher:
This book presents a collection of papers on various topics related to image and video processing. Both topics are of great interest to the image processing community, and video processing technologies are of particular interest to the multimedia and aerospace industries.

Nonlinear Model-Based Image/Video Processing and Analysis

Motivated by the rich, psycho-physiologically grounded properties of auditory cortical representations and the power of sparse representation-based classifiers, we propose a robust music genre classification framework. Its first pilar is a novel multilinear subspace analysis method that reduces the dimensionality of cortical representations of music signals, while preserving the topology of the cortical representations. Its second pilar is the sparse representation based classification, that models any test cortical representation as a sparse weighted sum of dictionary atoms, which stem from training cortical representations of known genre, by assuming that the representations of music recordings of the same genre are close enough in the tensor space they lie. Accordingly, the dimensionality reduction is made in a compatible manner to the working principle of the sparse-representation based classification. Music genre classification accuracy of 93.7% and 94.93% is reported on the GTZAN and the ISMIR2004 Genre datasets, respectively. Both accuracies outperform any accuracy ever reported for state of the art music genre classification algorithms applied to the aforementioned datasets.

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Constantine Kotropoulos

Papers

Morphological elastic graph matching applied to frontal face authentication under well-controlled and real conditions

Emotional speech classification using Gaussian mixture models

Mobile phone identification using recorded speech signals

Nonlinear Model-Based Image/Video Processing and Analysis

Music genre classification via Topology Preserving Non-Negative Tensor Factorization and sparse representations