An original approach to represent 2D and 3D faces using Radial Geodesic Distances (RGDs) is proposed in this work. In 3D, the RGD of a generic point of the face surface is computed as the length of the geodesic connecting the point with a reference point along a radial direction. In 2D, the RGD of a pixel with respect to a reference pixel accounts for the difference of gray level intensities of the two pixels and the Euclidean distance between them. Support Vector Machines (SVMs) are used to perform face recognition using 2D-and 3D-RGDs. Due to the high dimensionality of face representations based on RGDs, embedding into lower-dimensional spaces is applied before SVMs classification. Experimental results are reported for 3D-3D and 2D-3D face recognition using the proposed approach.
This paper presents a novel automatic pattern recognition system for the classification of herbal substances, which comprises the analysis of chemical data obtained from three analytical techniques such as Thin Layer Chromatography (TLC), Gas Chromatography (GC) and Ultraviolet Spectrometry (UV), composed of the following stages. First, a preprocessing stage takes place that ranges from the TLC plate image conversion into a spectrum to the normalization and alignment of spectral data for all techniques. Then, a hierarchical clustering procedure is applied for each technique with the goal of discovering groups or classes that provide evidence concerning the different existing types. Next, an entropy-based template selection step for each group was introduced to exclude the less significant samples, thus allowing to improve the quality of the training set for each technique. In this manner, each class is now described by a set of key prototypes that allows the field expert to have a more accurate characterization and understanding of the phenomenon. Moreover, an improvement of the computational complexity for training and prediction tasks of the Support Vector Machines (SVM) is also achieved. Finally, a SVM classifier is trained for each technique. The experiments conducted show the validity of the proposal, showing an improvement of the classification results on each technique.
Abstract. This work proposes the use of functional data analysis to represent 3D faces for recognition tasks. This approach allows exploiting and studying characteristics of the continuous nature of this type of data. The basic idea of our proposal is to approximate the 3D face surface through an expansion of a basis functions set. These functions are used for a global representation of the entire face, and a local representation, where pre-selected face regions are used to construct multiple local representations. In both cases, the functions are fitted to the 3D data by means of the least squares method. Univariate attribute selection is finally applied to reduce the dimensionality of the new representation. The experiments prove the validity of the proposed approach, showing competitive results with respect to the state of the art solutions. Moreover, the dimensionality of the data is considerably reduced with respect to the original size, which is one of the goals of using this approach.
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