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

Three simple speaker verification techniques based on vector quantization, sphericity models and dynamic time warping, respectively, are developed and tested using the same experimental protocol. Two types of feature vectors, the linear prediction derived cepstral coefficients and the melfrequency cepstral coefficients are considered. The efficiency of the combination of the type of acoustic analysis and the verification technique is quantitatively measured through the achieved equal error rate.

Comparative study of speaker verification techniques based on vector quantization, sphericity models

Three simple speaker verification tech- niques based on vector quantization, sphericity mod- els and dynamic time warping, respectively, are de- veloped and tested using the same experimental pro- tocol. Two types of feature vectors, the linear pre- diction derived cepstral coefficients and the mel- frequency cepstral coefficients are considered. The efficiency of the combination of the type of acoustic analysis and the verification technique is quantita- tively measured through the achieved equal error rate.

/pdf/comparativ-estud-yo-fspeake-rverificatio-ntechniques-base-do-3i041r94sc.pdf

Comparativ estud yo fspeake rverificatio ntechniques base do nvecto rquantization ,sphericit ymodel sand dynami ctim ewarping*

The representation of α-stable distributions as scale mixture of normals is exploited to model the noise in musical audio recordings. Markov Chain Monte Carlo inference is used to estimate the clean signal model and the α-stable noise model parameters in a sparse linear regression framework with structured priors. The musical audio recordings were processed both as a whole and in segments by using a sine-bell window for analysis and overlap-and-add reconstruction. Experiments on noisy Greek folk music excerpts demonstrate better denoising under the α-stable noise assumption than the Gaussian white noise one, when processing is performed in segments rather than in full recordings.

Musical Audio Denoising Assuming Symmetric α-Stable Noise.

Techniques in Knowledge-Based Signal/Image Processing and Their Application in Geophysical Image Interpretation

Multidimensional Scaling (MDS) is applied to pairwise dissimilarities between entities, aiming to map each entity to a point in a geometric space so that the inter-point distances preserve the pairwise dissimilarities. The well-known algorithms for solving the MDS problem are vulnerable to gross errors (outliers), inducing highly corrupted embeddings. To cope with such gross errors, two algorithms are proposed, which resort to half-quadratic optimization, employing M-estimators and nuclear norm regularization. It is demonstrated by experiments that the proposed algorithms outperform the state-of-the-art MDS ones.

Constantine Kotropoulos

Papers

Comparative study of speaker verification techniques based on vector quantization, sphericity models

Comparativ estud yo fspeake rverificatio ntechniques base do nvecto rquantization ,sphericit ymodel sand dynami ctim ewarping*

Musical Audio Denoising Assuming Symmetric α-Stable Noise.

Techniques in Knowledge-Based Signal/Image Processing and Their Application in Geophysical Image Interpretation

Robust multidimensional scaling employing M-estimators and nuclear norm regularization