Anastasios Maronidis

Learn 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)
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(More)
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)
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)
—The notion of signal sparsity has been gaining increasing interest in information theory and signal processing communities. Recent advances in fields like signal compression, sampling and analysis have accentuated the crucial role of sparse representations of signals. As a consequence, there is a strong need to measure sparsity and towards this end, a(More)
Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently, Discriminant Analysis (DA) methods, which use subclass information for the discrimination between the data classes, have attracted much attention. As DA methods are strongly dependent on the underlying(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)