Constantine Kotropoulos

Bio: Constantine Kotropoulos is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Support vector machine & Feature vector. The author has an hindex of 41, co-authored 245 publications receiving 5869 citations.

Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication

Abstract: A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines (SVM). Both linear and nonlinear SVM are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique.

Automatic emotional speech classification

Abstract: Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The sequential forward selection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the cross-validated correct classification score of one of the following classifiers: nearest mean and Bayes classifier where class pdf are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% /spl plusmn/ 3% correct classification rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those two-class emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them.

Rule-based face detection in frontal views

Abstract: Face detection is a key problem in building automated systems that perform face recognition A very attractive approach for face detection is based on multiresolution images (also known as mosaic images) Motivated by the simplicity of this approach, a rule-based face detection algorithm in frontal views is developed that extends the work of G Yang and TS Huang (see Pattern Recognition, vol27, no1, p53-63, 1994) The proposed algorithm has been applied to frontal views extracted from the European ACTS M2VTS database that contains the videosequences of 37 different persons It has been found that the algorithm provides a correct facial candidate in all cases However, the success rate of the detected facial features (eg eyebrows/eyes, nostrils/nose, and mouth) that validate the choice of a facial candidate is found to be 865% under the most strict evaluation conditions

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.

The Self-Organizing Map

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