FVeinSyn is a large‑scale synthetic finger‑vein image generator that separates vascular topology synthesis from imaging appearance rendering. It uses stochastic L‑systems to create anatomically valid, identity‑distinctive vein patterns, a cascaded region‑aware GAN to produce realistic near‑infrared images, and an intra‑class diversity generator to simulate realistic variations. The framework generated 500,000 images across 10,000 identities, and models trained with this data outperformed real‑data‑only baselines on eight public datasets, improving average accuracy by 27.43%.
By Yifan Wang, Jie Gui, Adams Wai Kin Kong, Baosheng Yu, Changsheng Chen, Qi Li, Zhenan Sun, James Tin-Yau Kwok, Alex Kot
OpenVeinNet is a finger vein verification framework that tackles open-set scenarios by combining Dynamic Snake Convolution, which extracts local curvilinear vein structures through adaptive sampling, with a graph convolutional backbone that models long-range topological relationships between vein regions. The model introduces a Centroid Angular Hybrid Loss to promote intra-class compactness and inter-class angular separation in the embedding space. Experiments on five public datasets under leave-one-dataset-out training demonstrate strong cross-dataset generalisation, low equal error rates, and competitive true accept rates at fixed false accept rates.
By Sushrut Patwardhan, Raghavendra Ramachandra
Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe externally, and relatively resistant to presentation a...
arXiv:2606. 11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access.
By Kumar Kartikey, Nikos Komninos
arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.
By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska
arXiv:2609.36400v1 Announce Type: cross
Abstract: Deep learning classifiers for dermoscopic skin lesions often reach high in-distribution accuracy while quietly relying on spurious background cues su...
By Youssef Attia, Debasmita Mukherjee
arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.
By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh
arXiv:2606. 01896v1 Announce Type: cross Abstract: Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or biased.
By Atmika Bhardwaj, Silvia Vock, Nico Steckhan
The paper introduces a dual‑stage deep learning system for detecting dental caries in panoramic radiographs. It first localizes teeth using Faster R‑CNN, then applies U‑Net for pixel‑wise caries segmentation, converting polygon annotations into high‑resolution binary masks. Trained on 3,000 images with both expert and algorithmic labels, the model achieves an IoU of 0.9013, Dice of 0.9482, Recall of 0.9433, and Precision of 0.9774, outperforming existing methods and reducing false positives.
By Jihun Kim, Kyeonghun Kim, Jong-yeol Lee, Yeongseok Seo, Dohyun Chun
arXiv:2607. 14932v1 Announce Type: cross Abstract: Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs.
By Pawe{\l} Borsukiewicz, Daniele Lunghi, Wendk\^uuni C. Ou\'edraogo, Jacques Klein, Tegawend\'e F. Bissyand\'e
arXiv:2606. 10713v1 Announce Type: cross Abstract: The nnU-Net has demonstrated continuous success in medical segmentation tasks, which heavily rely on the availability and diversity of annotated biomedical data.
By Ana Sofia Santos, Andr\'e Ferreira, Gijs Luijten, Naida Solak, Lisle Faray de Paiva, Behrus Hinrichs-Puladi, Jens Kleesiek, Jan Egger, Victor Alves
Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated images, especially for underrepresented skin tones and rare diseases, impedes progress toward measurably fair methods. We introduce cgDDI (Controllable Generation of Diverse Dermatological Imagery), a hybrid framework that (1) synthesizes realistic healthy skin samples without disturbing other input properties, (2) maps single-sample rare lesions onto novel skin-tones and locations non-parametrically, and (3) allows for efficient parametric generation with as few as 10 training samples.