In this paper we consider the aggregation functions' application to problems of face recognition. A wide range of aggregation function classes can play a crucial role in multi-criteria decision-making theory. Some of the most well-known aggregation functions include generalized means, integrals based on non-additive measures, etc. Their usage also saves memory resources and can lead to an increase in the percentage of correctly classified objects (in our case, we deal with object recognition). Biometry and face recognition represent important modern problems in computer science, and some solutions are widely-adopted and applied in mobile devices and identity confirmation, border control, ID and other document verification, etc. Other than classic methods for face recognition, such as PCA, linear discriminant analysis, as well as various local descriptors and other methods, methods based on sparse data representation and deep learning methods have recently been used. Each part of the face can be compared individually, and a conclusion regarding the match-overlap of two faces should be drawn from all these comparisons. In our investigation, we analyze the quality of the decisions made in face recognition problems, with regards to the aggregation functions used, as well as the different methods used for recognizing certain parts of the face.
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