## 1 Introduction  
Person re-identification (re-ID) is becoming one of the most significant research topics in the domains of computer vision and computational intelligence, due to its two-fold importance for both science and society. It focuses on person identification across camera systems by addressing the increasing demand for public safety. The problem regarding person identification commonly resides with the person detection and tracking tasks [1]. In respect of this, the reidentification task is commonly formulated as follows: a person in a query image is to be matched with a person in an image, or images, that are obtained from data streams of different cameras, or a given camera, at various moments in time. A database of images or videos is often used instead of live streams. The main challenge is to assess the similarity between objects while taking into account possible changes in human appearance due to variations in camera viewpoints, lighting conditions, the person's pose, and occlusions. In this paper, we focus on the similarity assessment; while for the database search techniques, the reader is referred to the literature [2] with a special mention of the emerging graph-based methods [3; 4]. In practice, re-ID person similarity evaluation is conducted using images obtained from video frames. Recently, various video re-ID approaches have emerged that effectively use temporal information in order to improve the assessment accuracy [5; 6]. Nevertheless, the visual similarity estimation remains the core of re-ID. In addition, the methods that perform well on images can be expected to perform even better when combined with temporal data. The fast development in the area of convolutional neural networks (CNN), as well as deep learning (DL), that affected numerous areas of computer science also led to the emergence of machine learning and DL-assisted re-ID techniques [7; 8]. Re-ID requires CNNs to assess the similarity for image pairs, which is not typical for conventional image classification tasks. This inspired researchers to propose novel network architectures, such as: Siamese networks [9; 10]; specialized loss functions, e.g., triplet loss [11; 12]; specific attention modules [13]; re-ID graph neural networks [14]; and others. Moreover, the augmented images were often included in training datasets in order to simulate illumination-related effects [15; 16]. Many early works focused on using CNNs for either feature extraction or metric learning [17; 18], with end-to-end models gradually becoming dominant in the field. However, generalization of the results is challenging for DL-assisted re-ID. That is, models trained on specific datasets tend to perform poorly on other data, as can be illustrated by the fact that models trained on Market1501 to 98% rank-1 accuracy reach only 38% accuracy on DukeMTMC [19]. The effects of negative transfer learning can be drastic due to significant variations in data contents, e.g., the different clothes people wear, different camera types, different filming environments, etc. A combination of such effects is often referred to as the \"Open-World re-ID problem\". In addressing this problem, a further complication of the CNN architecture [18; 20] is required. Therefore, models become extremely bulky with significant parameter redundancy that reduces speed, as well as increases computational power and storage demands. Thus, generalization implies overparameterization, which increases costs and does not guarantee good performance. In order to address this issue, we focus on analytical techniques to obtain a compact and fast-performing model. This is performed in order to reduce computational costs and to improve generalization. This is achieved by constructing compact interpretable object descriptors (i.e., feature vectors) that are combined with a similarity ranking scheme. Analytical models, while falling out of favor recently due to growing interest towards DL models, previously showed promising results on re-ID tasks [21; 22].