Anastasios Maronidis

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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)
—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)
—Recently, subspace learning methods for Dimensionality Reduction (DR), like Subclass Discrim-inant Analysis (SDA) and Clustering-based Discrimi-nant 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(More)