Anastasios Maronidis

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In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed. Although it is(More)
Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently, methods like Subclass Discriminant Analysis (SDA) and Clusteringbased Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention.(More)
In this paper, the problem of frontal view recognition on still images is confronted, using subspace learning methods. The aim is to acquire the frontal images of a person in order to achieve better results in later face or facial expression recognition. For this purpose, we utilize a relatively new subspace learning technique, Clustering based Discriminant(More)
In this paper, the robustness of appearance-based, subspace learning techniques for facial expression recognition in geometrical transformations is explored. A plethora of facial expression recognition algorithms is presented and tested using three well-known facial expression databases. Although, it is common-knowledge that appearance based methods are(More)
The use of image processing techniques in cultural heritage applications has been gaining increasing interest in the research community. In this paper, an integrated framework that can be used for virtual restoration of the facial region of damaged Byzantine icons is presented. A key aspect of the proposed methodology is the integration of practices adopted(More)
An integrated tool that can be used for damage detection, shape restoration and texture restoration of faces appearing in Byzantine icons, is presented. The damage detection process involves the estimation of residuals obtained after the coding and reconstruction of face image regions using trained Principal Component Analysis (PCA) texture models. Shape(More)
The rise of big data, which need computationally demanding manipulation has posed unprecedented challenges in the machine learning community. In this context, a variety of dimensionality reduction methods has been introduced in order to deal with the large-scale aspect of the data. However, their employment in very large scales often becomes impractical due(More)
Recently, subspace learning methods for Dimensionality Reduction (DR), like Subclass Discriminant Analysis (SDA) and Clustering-based Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work has been accomplished on Graph Embedding (GE), which is a(More)