6 accepted papers
The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can generate high-fidelity images and lifelike avatars…
The primary obstacle in realizing the full potential of finger crease biometrics is the accurate identification of deformed knuckle patterns, often resulting from completely contactless imaging. Current methods struggle significantly with this task, yet accurate matching is crucial for applications…
Most recent methods formulate the task of human pose estimation as a heatmap estimation problem, and use the overall L2 loss computed from the entire heatmap to optimize the heatmap prediction. In this paper, we show that in bottom-up human pose estimation where each heatmap often contains multiple…
In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shap…
Accurate detection of the fake but photorealistic images is one of the most challenging tasks to address social, biometrics security and privacy related concerns in our community. Earlier research has underlined the existence of spectral domain artifacts in fake images generated by powerful generati…
This paper proposes an accurate and generalizable deep learning framework for iris recognition. The proposed framework is based on a fully convolutional network (FCN), which generates spatially corresponding iris feature descriptors. A specially designed Extended Triplet Loss (ETL) function is intro…