The field of biometric identification and computer vision is rapidly evolving, with a focus on developing innovative methods for identifying and recognizing individuals, animals, and patterns. Recent research has explored the use of footprints, gait patterns, and semantic segmentation to advance personalized treatment, pet management, and wildlife conservation. Notably, there is a growing interest in leveraging deep neural networks, graph convolutional networks, and vision models to improve the accuracy and efficiency of these methods. Some noteworthy papers include: PawPrint, which introduces a non-invasive approach to pet identification through footprint analysis. ExoGait-MS, which proposes a novel method for personalized gait recognition using multi-scale graph networks. BiggerGait, which unlocks the potential of large vision models for gait recognition by utilizing layer-wise representations. LeMoRe, which presents an efficient paradigm for lightweight semantic segmentation by synergizing explicit and implicit modeling.