Tootfinder

Deep Learning Models for Robust Facial Liveness Detection
Oleksandr Kuznetsov, Emanuele Frontoni, Luca Romeo, Riccardo Rosati, Andrea Maranesi, Alessandro Muscatello
https://arxiv.org/abs/2508.09094

Deep Learning Models for Robust Facial Liveness Detection
In the rapidly evolving landscape of digital security, biometric authentication systems, particularly facial recognition, have emerged as integral components of various security protocols. However, the reliability of these systems is compromised by sophisticated spoofing attacks, where imposters gain unauthorized access by falsifying biometric traits. Current literature reveals a concerning gap: existing liveness detection methodologies - designed to counteract these breaches - fall short again…

Identification of Gamma Ray Pulsar Candidates in the \emph{Fermi}-LAT 4FGL-DR4 Unassociated Sources Using Supervised Machine Learning
A. Pathania, K. K. Singh, S. K. Singh, A. Tolamatti, B. B. Singh, K. K. Yadav
https://arxiv.org/abs/2510.08654

Identification of Gamma Ray Pulsar Candidates in the \emph{Fermi}-LAT 4FGL-DR4 Unassociated Sources Using Supervised Machine Learning
The Large Area Telescope (LAT) on board the \emph{Fermi} Gamma-ray Space Telescope has been continuously providing good quality survey data of the entire sky in the high energy range from 30 MeV to 500 GeV and above since August 2008. A succession of gamma-ray source catalogs is published after a comprehensive analysis of the \emph{Fermi}--LAT data. The most recent release of data in the fourth \emph{Fermi}--LAT catalog of gamma-ray sources (4FGL-DR4), based on the first 14 years of obser…

Multiplexed back focal plane imaging with on-chip integrated microlens array
Magdalena Furman, Marcin Muszy\'nski, Przemys{\l}aw Oliwa, {\L}ukasz Zinkiewicz, Aleksander Bogucki, Jacek Szczytko, Piotr Wasylczyk, Wojciech Pacuski, Mateusz Kr\'ol, Barbara Pi\k{e}tka
https://arxiv.org/abs/2510.09348

Multiplexed back focal plane imaging with on-chip integrated microlens array
On-chip optical architectures that enable angle-resolved spectroscopy are essential for advancing photonic platforms towards low-volume, scalable, and cryo-compatible devices. Here, we introduce spatially resolved momentum-space imaging using arrays of 3D-printed microlenses directly integrated onto semiconductor optical microcavities. Each microlens functions as an independent optical element with a high numerical aperture (approx. 0.95), enabling parallel back focal plane imaging across, in o…

Improving Facial Rig Semantics for Tracking and Retargeting
Dalton Omens, Allise Thurman, Jihun Yu, Ronald Fedkiw
https://arxiv.org/abs/2508.08429 https://…

Improving Facial Rig Semantics for Tracking and Retargeting
In this paper, we consider retargeting a tracked facial performance to either another person or to a virtual character in a game or virtual reality (VR) environment. We remove the difficulties associated with identifying and retargeting the semantics of one rig framework to another by utilizing the same framework (3DMM, FLAME, MetaHuman, etc.) for both subjects. Although this does not constrain the choice of framework when retargeting from one person to another, it does force the tracker to use…

Multimodal Policy Internalization for Conversational Agents
Zhenhailong Wang, Jiateng Liu, Amin Fazel, Ritesh Sarkhel, Xing Fan, Xiang Li, Chenlei Guo, Heng Ji, Ruhi Sarikaya
https://arxiv.org/abs/2510.09474

Multimodal Policy Internalization for Conversational Agents
Modern conversational agents like ChatGPT and Alexa+ rely on predefined policies specifying metadata, response styles, and tool-usage rules. As these LLM-based systems expand to support diverse business and user queries, such policies, often implemented as in-context prompts, are becoming increasingly complex and lengthy, making faithful adherence difficult and imposing large fixed computational costs. With the rise of multimodal agents, policies that govern visual and multimodal behaviors are …

Multi-Scale Land Use Impacts on Fossil Fuel-Related CO$_2$ Emissions in the United States
Jason Hawkins, Mehrnoosh Zare
https://arxiv.org/abs/2510.08611 https://

Multi-Scale Land Use Impacts on Fossil Fuel-Related CO$_2$ Emissions in the United States
Anthropogenic greenhouse gas (GHG) emissions exhibit spatial variation owing to differences in development patterns, local climate, economic composition, energy sources, and other factors. Many of these factors - and therefore their contribution to GHG production - are influenceable through spatial planning and economic policy. Recent advances in environmental data reporting and climate flux measurement have produced high fidelity GHG emissions estimates at detailed spatial and temporal resolut…

Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality
Chao Wang, Michael Gienger, Fan Zhang
https://arxiv.org/abs/2508.08999 h…

Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality
Expressive behaviors in robots are critical for effectively conveying their emotional states during interactions with humans. In this work, we present a framework that autonomously generates realistic and diverse robotic emotional expressions based on expert human demonstrations captured in Mixed Reality (MR). Our system enables experts to teleoperate a virtual robot from a first-person perspective, capturing their facial expressions, head movements, and upper-body gestures, and mapping these b…

LIA-X: Interpretable Latent Portrait Animator
Yaohui Wang, Di Yang, Xinyuan Chen, Francois Bremond, Yu Qiao, Antitza Dantcheva
https://arxiv.org/abs/2508.09959 https://

LIA-X: Interpretable Latent Portrait Animator
We introduce LIA-X, a novel interpretable portrait animator designed to transfer facial dynamics from a driving video to a source portrait with fine-grained control. LIA-X is an autoencoder that models motion transfer as a linear navigation of motion codes in latent space. Crucially, it incorporates a novel Sparse Motion Dictionary that enables the model to disentangle facial dynamics into interpretable factors. Deviating from previous 'warp-render' approaches, the interpretability of the Spars…

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Masoumeh Sharafi, Soufiane Belharbi, Houssem Ben Salem, Ali Etemad, Alessandro Lameiras Koerich, Marco Pedersoli, Simon Bacon, Eric Granger
https://arxiv.org/abs/2508.09202

Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
Facial expression recognition (FER) models are employed in many video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting their performance in real-world applications. To improve their performance, source-free domain adaptation (SFDA) methods have been proposed to personalize a pretrained source model using only unlabeled target domain da…

Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation
Seyed Muhammad Hossein Mousavi, S. Younes Mirinezhad
https://arxiv.org/abs/2508.09188

Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation
Affective computing faces a major challenge: the lack of high-quality, diverse depth facial datasets for recognizing subtle emotional expressions. We propose a framework for synthetic depth face generation using an optimized GAN with Knowledge Distillation (EMA teacher models) to stabilize training, improve quality, and prevent mode collapse. We also apply Genetic Algorithms to evolve GAN latent vectors based on image statistics, boosting diversity and visual quality for target emotions. The ap…