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20222026
most citedImproving Depression estimation from facial videos with face alignment, training optimization and scheduling

3 citations · 7 across the 9 of their papers we have counts for

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cs.CV2026

Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

Constantino Álvarez Casado, Mohammad Rahman, Sasan Sharifipour +4

Human-machine interfaces in industrial automation need sensing modules that monitor operator actions and physiological state. This is important in factories, vehicles, machinery ca…

cs.CV2025

Quality-Aware Framework for Video-Derived Respiratory Signals

Nhi Nguyen, Constantino Álvarez Casado, Le Nguyen +2

Video-based respiratory rate (RR) estimation is often unreliable due to inconsistent signal quality across extraction methods. We present a predictive, quality-aware framework that…

cs.CV2025

LiDAR-based Human Activity Recognition through Laplacian Spectral Analysis

Sasan Sharifipour, Constantino Álvarez Casado, Le Nguyen +4

Human Activity Recognition supports applications in healthcare, manufacturing, and human-machine interaction. LiDAR point clouds offer a privacy-preserving alternative to cameras a…

cs.CV2024

OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis

Manuel Lage Cañellas, Le Nguyen, Anirban Mukherjee +6

In the domain of non-contact biometrics and human activity recognition, the lack of a versatile, multimodal dataset poses a significant bottleneck. To address this, we introduce th…

cs.CV2023

Estimating exercise-induced fatigue from thermal facial images

Manuel Lage Cañellas, Constantino Álvarez Casado, Le Nguyen +1

Exercise-induced fatigue resulting from physical activity can be an early indicator of overtraining, illness, or other health issues. In this article, we present an automated metho…

cs.CV2022★ 3 cited

Improving Depression estimation from facial videos with face alignment, training optimization and scheduling

Manuel Lage Cañellas, Constantino Álvarez Casado, Le Nguyen +1

Deep learning models have shown promising results in recognizing depressive states using video-based facial expressions. While successful models typically leverage using 3D-CNNs or…