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Local descriptors are widely used technique of feature extraction to obtain information about both local and global properties of an object. Here, we discuss an application of the Chain Code-Based Local Descriptor to face recognition by focusing on various datasets and considering different variants of this description method. We augment the generic form of the descriptor by adding a possibility of grouping pixels into blocks, i.e., effectively describing larger neighborhoods. The results of experiments show the efficiency of the approach. We demonstratethat the obtained results are comparable or even better than those delivered by other important algorithms in the class of methods based on the Bag-of-Visual-Words paradigm.

In this study, we develop a process of estimation of importance of features considered in face recognition by making use of the analytic hierarchy process (AHP). The AHP method of pairwise comparisons realized at three levels of hierarchy becomes crucial to realize a comprehensive weighting of cues so that sound estimates of weights associated with the individual features of faces can be formed. We demonstrate how to carry out an efficient process of face description by using a collection of linguistic descriptors of the features and their groups. Numerical dependencies between the features are quantified with the help of experienced criminology and psychology experts. Finally, we present an entropy-based method of evaluation of the relevance of the estimation process completed by the individuals.

In this study, we introduce a recent multicriteria decision theory concept of a new, generalized form of Choquet integral function and its application, in particular to the problem of face classification based on the aggregation of classifiers. Such function may be constructed by a simple replacement of the product used under the Choquet integral sign by any t-norm. This idea brings forward a broad class of aggregation operators, which can be incorporated into the decision-making theory. In this context, in a series of experiments we compare the most known t-norms and thoroughly examine their performance in the process of combining individual classifiers based either on facial regions or classic face recognition methods. Such kind of generalization can successfully improve the classification process provided that the parameters of the t-norms are carefully adjusted.

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Behavioral traits play a major role in successful adaptation of wildlife to urban conditions. However, there are few studies showing how urban conditions affect the social behavior of urban animals during their direct encounters. It is generally believed that the higher density of urban populations translates into increased aggression between individuals. In this paper, using a camera-trap method, we compared the character of direct encounters in urban and non-urban populations of the striped field mouse Apodemus agrarius (Pallas, 1771), a species known as an urban adapter. We confirmed the thesis that urbanization affects the social behavior and urban and rural populations differ from each other. Urban animals are less likely to avoid close contact with each other and are more likely to show tolerant behavior. They also have a lower tendency towards monopolization of food resources. The behavior of urban animals varies depending on the time of day: in the daytime, animals are more vigilant and less tolerant than at night. Our results indicate that, in the case of the species studied, behavioral adaptation to urban life is based on increasing tolerance rather than aggression in social relations. However, the studied urban adapter retains the high plasticity of social behavior revealed even in the circadian cycle. The observation that tolerance rather than aggression may predominate in urban populations is a new finding, while most studies suggest an increase in aggression in urban animals. This opens an avenue for formulating new hypotheses regarding the social behavior of urban adapters.