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Abstract. The paper addresses the problem of face recognition for images registered in variable lighting, which is common for real-world conditions. Presented algorithm is based on orthogonal transformation preceded by simple transformations comprising of equalization of brightness gradients, removal of spatial low frequency spectral components and fusion of spectral features depending on average pixels intensity. Two types of transformations: 2DDCT (two-dimensional Discrete Cosine Transform) and 2DKLT (two-dimensional Karhunen-Loeve Transform) were investigated in order to find the most optimal algorithm setup. The results of experiments conducted on Yale B and Yale B+ datasets show that a quite simple algorithm is capable of successful recognition without high computing power demand, as opposite to several more sophisticated methods presented recently.

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Currently, 3D scanning technology is used for high-precision measurements and fixing the geometric shape of various objects. However, when creating a computer 3D-model as a result of processing the array of data obtained from the scanning process, may contain errors. Errors may related to the features of the studied object (material, weight, size, location), functional properties of the software used or they may be the low qualification of the software engineer involved in processing 3D scan data. The task of this work was a finding the technical solutions, which allows one to reconstruct the objects surface, recorded using 3D scanning in the process of creating their computer 3D models. We proposed to use the inverse distance method, which is used in biometry to improve the quality of reconstruction of the 3D surface of a human face. Experiments have shown that the result of the reconstruction makes it possible to increase the accuracy of creating 3D models by recovering gaps in the surface of an object that were made during the 3D scanning process. The proposed approach allows one to construct a 3D surface without solving the triangulation problem, which leads to a reduction in computational costs.