This Special Section of the Pattern Recognition Letters journal includes the best papers awarded at the 3rd International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) held in Paris, France, during 1-3 June 2022. A total of 153 papers were presented at the ICPRAI 2022 conference. At the closing ceremony, Manal Hamzaoui et al., received the best paper award for their manuscript "A Hierarchical Prototypical Network for Few-Shot Remote Sensing Scene Classification". The second best paper was awarded to Juan Olmos et al., for their paper "An Oculomotor Digital Parkinson Biomarker: From a Deep Riemannian Representation.", while the third best paper award was granted to Victor Fortier et al., for their paper "Robust Detection of Conversational Groups using a Voting Scheme and a Memory Process". The three awarded papers were invited to submit an extended manuscript to the Special Section of Pattern Recognition Letters journal. After an exhaustive review process involving two rounds, the three extended papers were accepted for publication. The paper by Hamzaoui et al. [1] proposes a hyperbolic prototypical network for few fhot remote sensing scene classification. The objective of this work was to examine the relevance of hyperbolic embeddings of Remote Sensing (RS) data, in the context of the few-shot remote sensing scene classification problem. To this end, the authors have adopted hyperbolic prototypical networks as a meta-learning approach to embed scene images along with a feature clipping technique to ensure a numerically steady model. These hyperbolic embeddings were then analyzed whether they provide a better representation than Euclidean representations and better reflect the underlying structure of scene classes. The experimental results on the NWPU-RESISC45 RS dataset have demonstrated that the hyperbolic embeddings outperform their Euclidean counterparts. This study suggests, therefore, that operating in hyperbolic spaces is an interesting alternative for the RS community The paper by Olmos et al. [2] proposes a Riemannian SPD learning scheme to characterize fixational oculomotor Parkinsonian abnormalities. It introduces a deep Riemannian framework to discover potential oculomotor patterns from non-invasive video analysis, with the aim at mitigating data scarcity and interpreting the latent space. A convolutional representation is first built and aggregated onto a symmetric positive definite matrix (SPD) to encode deep convolutional features’ second-order statistics. The latter are then fed to a non-linear hierarchical architecture that processes SPD data by maintaining them into their Riemannian manifold. This representation allows discriminating patients with Parkinson's disease (PD) from Healthy Controls, including at PD stages 2.5 and 3. Moreover, the proposed geometrical representation shows capabilities to statistically differentiate observations among Parkinson's stages. The developed tool also demonstrates coherent results from explainability maps back-propagated from output probabilities. The paper by Tosato et al. [3] proposes an approach for exploiting temporal information to detect conversational groups in videos and predict the next speaker. The work's first objective is to detect, in video sequences, F-formations, which are concepts describing spatial arrangements of participants during social interactions. The second objective is to predict the next speaker during a group conversation. The approach proposed by the authors harnesses time information and multimodal signals of humans extracted from video sequences, and uses the engagement level of people as a group belonging feature. It relies on a Long Short Term Memory neural network to predict who will take the speaker's turn in a conversation group. When tested on the MatchNMingle dataset, this scheme achieves an accuracy of 85% true positives in group detection and 98 % in predicting the next speaker. We want to thank the authors of the papers for submitting their work to this Special Section. We also want to thank the reviewers for their efforts in providing critical and constructive feedback. Finally, we thank Maria de Marsico and Mohammed Samiullah for supporting this Special Section.