Diego R. Faria

2.2k total citations
55 papers, 936 citations indexed

About

Diego R. Faria is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Diego R. Faria has authored 55 papers receiving a total of 936 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 23 papers in Computer Vision and Pattern Recognition and 14 papers in Cognitive Neuroscience. Recurrent topics in Diego R. Faria's work include Human Pose and Action Recognition (12 papers), Hand Gesture Recognition Systems (11 papers) and Robot Manipulation and Learning (10 papers). Diego R. Faria is often cited by papers focused on Human Pose and Action Recognition (12 papers), Hand Gesture Recognition Systems (11 papers) and Robot Manipulation and Learning (10 papers). Diego R. Faria collaborates with scholars based in United Kingdom, Portugal and Brazil. Diego R. Faria's co-authors include Jordan J. Bird, Anikó Ekárt, Cristiano Premebida, Urbano Nunes, Eduardo Parente Ribeiro, Jorge Dias, Luis J. Manso, Jorge Lobo, Nicola Bellotto and Paulo Peixoto and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and IEEE Access.

In The Last Decade

Diego R. Faria

53 papers receiving 910 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Diego R. Faria United Kingdom 18 351 262 246 215 181 55 936
Kyoobin Lee South Korea 18 187 0.5× 308 1.2× 146 0.6× 205 1.0× 117 0.6× 59 1.1k
Minoru Fukumi Japan 15 545 1.6× 229 0.9× 203 0.8× 181 0.8× 162 0.9× 234 1.1k
Hussein Al Osman Canada 19 183 0.5× 327 1.2× 121 0.5× 269 1.3× 179 1.0× 66 1.2k
Pablo Barros Germany 18 506 1.4× 138 0.5× 247 1.0× 140 0.7× 179 1.0× 58 1.0k
Tevfik Metin Sezgin Türkiye 19 429 1.2× 322 1.2× 183 0.7× 136 0.6× 369 2.0× 81 1.2k
Emmanuel Dellandréa France 15 487 1.4× 107 0.4× 246 1.0× 132 0.6× 94 0.5× 31 919
Kaixuan Chen Australia 11 579 1.6× 468 1.8× 442 1.8× 224 1.0× 172 1.0× 20 1.4k
David Demirdjian United States 17 739 2.1× 99 0.4× 216 0.9× 144 0.7× 432 2.4× 35 1.1k
Carl Henrik Ek Sweden 18 419 1.2× 110 0.4× 285 1.2× 233 1.1× 120 0.7× 67 980

Countries citing papers authored by Diego R. Faria

Since Specialization
Citations

This map shows the geographic impact of Diego R. Faria's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Diego R. Faria with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego R. Faria more than expected).

Fields of papers citing papers by Diego R. Faria

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diego R. Faria. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Diego R. Faria. The network helps show where Diego R. Faria may publish in the future.

Co-authorship network of co-authors of Diego R. Faria

This figure shows the co-authorship network connecting the top 25 collaborators of Diego R. Faria. A scholar is included among the top collaborators of Diego R. Faria based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Diego R. Faria. Diego R. Faria is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Faria, Diego R., et al.. (2025). Advancing Emotionally Aware Child–Robot Interaction with Biophysical Data and Insight-Driven Affective Computing. Sensors. 25(4). 1161–1161. 3 indexed citations
2.
Premebida, Cristiano, et al.. (2025). Causality from bottom to top: a survey. Machine Learning. 114(11).
3.
Faria, Diego R., et al.. (2024). Multimodal Affective Communication Analysis: Fusing Speech Emotion and Text Sentiment Using Machine Learning. Applied Sciences. 14(15). 6631–6631. 6 indexed citations
4.
Premebida, Cristiano, et al.. (2022). Reducing Overconfidence Predictions in Autonomous Driving Perception. IEEE Access. 10. 54805–54821. 5 indexed citations
5.
Bird, Jordan J., Michael Pritchard, Antonio Fratini, Anikó Ekárt, & Diego R. Faria. (2021). Synthetic Biological Signals Machine-Generated by GPT-2 Improve the Classification of EEG and EMG Through Data Augmentation. IEEE Robotics and Automation Letters. 6(2). 3498–3504. 41 indexed citations
6.
Bustos, Pablo, et al.. (2021). A graph neural network to model disruption in human-aware robot navigation. Institutional Repository University of Extremadura (University of Extremadura). 16 indexed citations
7.
Patten, Timothy, Diego R. Faria, Fulvio Mastrogiovanni, et al.. (2021). Hand-Object Interaction: From Human Demonstrations to Robot Manipulation. Frontiers in Robotics and AI. 8. 714023–714023. 13 indexed citations
8.
Bird, Jordan J., et al.. (2020). Cross-Domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG. IEEE Access. 8. 54789–54801. 73 indexed citations
9.
Bird, Jordan J., et al.. (2020). Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach. PLoS ONE. 15(10). e0241332–e0241332. 18 indexed citations
10.
Bird, Jordan J., et al.. (2020). From Simulation to Reality: CNN Transfer Learning for Scene Classification. Nottingham Trent University's Institutional Repository (Nottingham Trent Repository). 619–625. 12 indexed citations
11.
Bird, Jordan J. & Diego R. Faria. (2019). A Bioinspired Approach for Mental Emotional State Perception towards Social Awareness in Robotics. 2. 8–11. 1 indexed citations
12.
Faria, Diego R., et al.. (2018). Emotion Recognition using Spatiotemporal Features from Facial Expression Landmarks. 789–794. 12 indexed citations
13.
Faria, Diego R., et al.. (2017). Affective facial expressions recognition for human-robot interaction. 805–810. 27 indexed citations
14.
Faria, Diego R., et al.. (2016). Combining discriminative spatiotemporal features for daily life activity recognition using wearable motion sensing suit. Pattern Analysis and Applications. 20(4). 1179–1194. 23 indexed citations
15.
Premebida, Cristiano, Diego R. Faria, Francisco Souza, & Urbano Nunes. (2015). Applying probabilistic Mixture Models to semantic place classification in mobile robotics. 4265–4270. 14 indexed citations
16.
Faria, Diego R., Cristiano Premebida, & Urbano Nunes. (2014). A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. 732–737. 64 indexed citations
17.
Faria, Diego R., et al.. (2014). Knowledge-based reasoning from human grasp demonstrations for robot grasp synthesis. Robotics and Autonomous Systems. 62(6). 794–817. 15 indexed citations
18.
Faria, Diego R., et al.. (2011). Extracting data from human manipulation of objects towards improving autonomous robotic grasping. Robotics and Autonomous Systems. 60(3). 396–410. 37 indexed citations
19.
Faria, Diego R., et al.. (2010). Probabilistic representation of 3D object shape by in-hand exploration. Estudo Geral (Universidade de Coimbra). 1560–1565. 9 indexed citations
20.
Faria, Diego R., Hadi Aliakbarpour, & Jorge Dias. (2009). Grasping movements recognition in 3D space using a Bayesian approach. e-Archivo (Carlos III University of Madrid). 1–6. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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