most citedUsing Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection

4 citations · 5 across the 5 of their papers we have counts for

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5 papers

cs.CV2026

Background-Free Objectness Learning for Class-Agnostic Detection

Dania Batool, Liliana Lo Presti, Marco La Cascia +1

Object detectors are typically trained under closed-set supervision, where unlabeled regions are implicitly treated as background. Under incomplete annotations, this assumption int…

cs.CV2026

Investigating Anisotropy in Visual Grounding under Controlled Counterfactual Perturbations

Gabriele Lombardo, Luigi Maiorana, Liliana Lo Presti +1

Visual Grounding benchmarks assume that the object described by a referring expression is always present in the image, and grounding models are therefore rarely evaluated under sem…

cs.CV2026

Sphere-Depth: A Benchmark for Depth Estimation Methods with Varying Spherical Camera Orientations

Soulayma Gazzeh, Giuseppe Mazzola, Liliana Lo Presti +1

Reliable depth estimation from spherical images is crucial for 360° vision in robotic navigation and immersive scene understanding. However, the onboard spherical camera can experi…

cs.CV20151 cited

Ensemble of Hankel Matrices for Face Emotion Recognition

Liliana Lo Presti, Marco La Cascia

In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These…

cs.CV20154 cited

Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection

Liliana Lo Presti, Marco La Cascia

This paper proposes a new approach to model the temporal dynamics of a sequence of facial expressions. To this purpose, a sequence of Face Image Descriptors (FID) is regarded as th…