Identifying criminals and victims in images (e.g., child pornography and masked gunmen) can be a challenging task, especially when neither their faces nor tattoos are observable. Skin mark patterns and blood vessel patterns are recently proposed to address this problem. However, they are invisible in low-resolution images and dense androgenic hair can cover them completely. Medical research results have implied that androgenic hair patterns are a stable biometric trait and have potential to overcome the weaknesses of skin mark patterns and blood vessel patterns. To the best of our knowledge, no one has studied androgenic hair patterns for criminal and victim identification before. This paper aims to study matching performance of androgenic hair patterns in low-resolution images. An algorithm designed for this paper uses Gabor filters to compute orientation fields of androgenic hair patterns, histograms on a dynamic grid system to describe their local orientation fields, and the blockwise Chi-square distance to measure the dissimilarity between two patterns. The 4552 images from 283 different legs with resolutions of 25, 18.75, 12.5, and 6.25 dpi were examined. The experimental results indicate that androgenic hair patterns even in low-resolution images are an effective biometric trait and the proposed Gabor orientation histograms are comparable with other well-known texture recognition methods, including local binary patterns, local Gabor binary patterns, and histograms of oriented gradients.

Few-shot learning (FSL) is a new machine learning method that applies the prior knowledge from some different domains tasks. The existing FSL models of metric-based learning have some drawbacks, such as the extracted features cannot reflect the true data distribution and the generalization ability is weak. In order to solve the problem in the present, we developed a model named cross modal adaptive few-shot learning based on task dependence (COOPERATE for short). A feature extraction and task representation method based on task condition network and auxiliary co-training is proposed. Semantic representation is added to each task by combining both visual and textual features. The measurement scale is adjusted to change the property of parameter update of the algorithm. The experimental results show that the COOPERATE has the better performance comparing with all approaches of the monomode and modal alignment FSL.

The conservation of the giant panda (Ailuropoda melanoleuca), as an iconic vulnerable species, has received great attention in the past few decades. As an important part of the giant panda population survey, the age distribution of giant pandas can not only provide useful instruction but also verify the effectiveness of conservation measures. The current methods for determining the age groups of giant pandas are mainly based on the size and length of giant panda feces and the bite value of intact bamboo in the feces, or in the case of a skeleton, through the wear of molars and the growth line of teeth. These methods have certain flaws that limit their applications. In this study, we developed a deep learning method to study age group classification based on facial images of captive giant pandas and achieved an accuracy of 85.99% on EfficientNet. The experimental results show that the faces of giant pandas contain some age information, which mainly concentrated between the eyes of giant pandas. In addition, the results also indicate that it is feasible to identify the age groups of giant pandas through the analysis of facial images.

The giant panda, a rare and iconic species endemic to China, has attracted significant attention from both domestic and international researchers due to its crucial ecological role, unique cultural value, and distinct evolutionary history. While substantial progress has been made in the field of individual identification, behavior recognition remains underdeveloped, facing challenges such as the lack of dynamic temporal features and insufficient extraction of behavioral characteristics. To address these challenges, we propose the Swin-Panda model, which leverages transfer learning based on the Video Swin Transformer architecture within the mmaction2 framework. In addition, we introduce two novel modules: the Comprehensive Perception Auxiliary Module and the Spatiotemporal Shift Attention Module. These modules facilitate the extraction of local and spatiotemporal information, allowing the model to more effectively capture the behavioral and movement patterns of giant pandas. Experimental results on the PACV-8 dataset demonstrate that our model achieves an accuracy of 88.02%, outperforming several benchmark models. This approach significantly enhances behavior recognition accuracy, thereby contributing to the advancement of panda welfare and species conservation efforts.