Vein patterns are gaining attention as a biometric modality, particularly in forensic investigations involving child sexual abuse cases where other biometric traits are often unavailable. Despite their strength as an identification method, extracting vein patterns from standard RGB images remains a challenging task. Although various techniques have been proposed to visualise veins from colour images, most predominantly rely on convolutional architectures. While these models effectively capture fine-grained local pixel details, they often struggle to preserve long-range dependencies and broader spatial context. However, when dealing with complex vein structures, intricate branching patterns, and extended regions, capturing global dependencies becomes essential for accurate visualisation.In this paper, we propose a hybrid Transformer Encoder Embedded Conditional Generative Adversarial Network (CTrans-GAN), which integrates the strengths of both convolutional networks and transformers through a self-attention mechanism. The model is trained on an RGB-NIR paired image dataset from 301 subjects and evaluated on arm images across three variations: small 100×100 crops, forearm regions, and full arm images. The performance of the proposed model is assessed using a range of objective metrics, including contrast accuracy, PSNR, SSIM, and vein length accuracy. The evaluation results demonstrate that the proposed model not only outperforms the previous GAN model on the 100×100 cropped images but also excels in visualising veins across larger skin areas, including the forearm and full arm, achieving over 81% vein length accuracy across all three dataset variations.

This paper undertakes a thorough analysis of the existing landscape of datasets within the beekeeping field, with the primary aim of furnishing precision beekeeping researchers with a comprehensive overview. Through an assessment of accessibility, scope, and applicability, this study endeavors to identify gaps, discern trends, and pinpoint potential areas for future exploration. The overarching goal is to propel advancements in bee health, productivity, and conservation through data-driven strategies. Stressing the pivotal role of technology in augmenting dataset effectiveness, the paper underscores the necessity for collaborative endeavors to standardize data collection and sharing practices. Serving as a cornerstone resource, this review aims to equip researchers with the necessary insights to harness data for the improvement of beekeeping practices and the sustainability of bee populations.

The rise of generative artificial intelligence (GenAI) has made it increasingly possible to use Deepfakes technology to generate fake pictures and videos. While this technology has benefits, it also has downsides such as spreading misinformation and endangering public interests. To address this issue, researchers have proposed various deep forgery detection algorithms and have achieved remarkable results. However, a common problem regarding these detection methods is that while in-library detection can usually achieve high accuracy, their performance is significantly degraded in cross-library detection. This indicates a severe problem of insufficient generalisation ability.To better compare the performance differences between various detection methods, this paper analyses the detection performance of the six established models of Two-stream, MesoNet, HeadPose, FWA, VA, and Multi-task. To ensure consistency, we employ a uniform evaluation framework as a benchmark for comparison. We conduct extensive intra-library and cross-library tests to evaluate these methods’ generalisation ability by utilising accuracy and error rate as key evaluation criteria for our experiments. Additionally, we further explore areas for improvement by analysing the impact of data augmentation, dataset partitioning, and threshold selection on the performance of these detection methods. Our comparative experiments are conducted on three existing fake face video datasets, including FaceForensics++, DeepfakeTIMIT, and Celeb-DF.Our research findings indicate the database partitioning method has a direct impact on the detector’s performance, and to enhance generalisation performance, the database should be divided person-based manually. The effectiveness of data augmentation techniques in improving cross-library performance is generally limited, and setting the threshold directly using source domain data often leads to a high error rate in the target domain. The findings of this paper provide insights into the development of more effective detection methods to combat the harmful effects of Deepfakes.

Child sexual abuse is a serious global problem that has gained public attention in recent years. Due to the popularity of digital cameras, many perpetrators take images of their sexual activities. Traditionally, it has been difficult to use vein patterns in evidence images for forensic identification, because they were nearly invisible in colour images. State-of-the-art techniques, and computational methods including optical-based vein uncovering or artificial neural networks have recently been introduced to extract vein patterns for identification purposes. However, these methods are still not mature due to limitations such as lack of reliable feature extraction, efficient uncovering algorithms, and matching difficulties. In this paper, we propose two new schemes to overcome some of these limitations by using sparse auto-encoder and adaptive contrast enhancement. Specifically, an adjustment sparse auto-encoder parameters scheme is used for optimising parameters, and then optimised parameters are automatically trained to enhance the robustness of vein visualisation and feature extraction. We also use a pair of synchronised colour and near infrared NIR images to generate the skeletonised vein patterns for verifying the outcome of the proposed method. The proposed algorithm was examined on a database with 100 pairs of colour and NIR images collected from different parts of the body such as forearms, thighs, chests and ankles. The experimental results are encouraging and indicate that the proposed method improves the feature extraction procedure, which can lead to better uncovering results compared with current methods.

Authentication methods based on some human traits, including fingerprint, face, iris, and palmprint, have been developed significantly, and currently, they are mature enough which have been reliably considered for person identification purposes. Recently, as a new research area, few methods based on non-facial skin features such as vein patterns have been developed. This extended abstract briefly explores some key features of biometric traits whereas vein pattern recognition is also outlined.