Raffaella Lanzarotti

1.2k total citations
42 papers, 602 citations indexed

About

Raffaella Lanzarotti is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computational Mechanics. According to data from OpenAlex, Raffaella Lanzarotti has authored 42 papers receiving a total of 602 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 12 papers in Signal Processing and 9 papers in Computational Mechanics. Recurrent topics in Raffaella Lanzarotti's work include Face recognition and analysis (20 papers), Face and Expression Recognition (19 papers) and Biometric Identification and Security (7 papers). Raffaella Lanzarotti is often cited by papers focused on Face recognition and analysis (20 papers), Face and Expression Recognition (19 papers) and Biometric Identification and Security (7 papers). Raffaella Lanzarotti collaborates with scholars based in Italy, France and United Arab Emirates. Raffaella Lanzarotti's co-authors include Paola Campadelli, Giuliano Grossi, Giuseppe Boccignone, Alessandro D’Amelio, Jianyi Lin, Vittorio Cuculo, Donatello Conte, Nicoletta Noceti, Francesca Odone and Paolo Napoletano and has published in prestigious journals such as PLoS ONE, IEEE Access and Sensors.

In The Last Decade

Raffaella Lanzarotti

40 papers receiving 583 citations

Peers

Raffaella Lanzarotti
Comparison fields: 5 of 84
  • Computer Vision and Pattern Recognition 302
  • Biomedical Engineering 153
  • Cardiology and Cardiovascular Medicine 114
  • Signal Processing 104
  • Human-Computer Interaction 95
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Citations per field, relative to Raffaella Lanzarotti
Raffaella Lanzarotti · 1×
Citations per year, relative to Raffaella Lanzarotti
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Countries citing papers authored by Raffaella Lanzarotti

Since Specialization
Citations

This map shows the geographic impact of Raffaella Lanzarotti'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 Raffaella Lanzarotti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raffaella Lanzarotti more than expected).

Fields of papers citing papers by Raffaella Lanzarotti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Raffaella Lanzarotti. 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 Raffaella Lanzarotti. The network helps show where Raffaella Lanzarotti may publish in the future.

Co-authorship network of co-authors of Raffaella Lanzarotti

This figure shows the co-authorship network connecting the top 25 collaborators of Raffaella Lanzarotti. A scholar is included among the top collaborators of Raffaella Lanzarotti 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 Raffaella Lanzarotti. Raffaella Lanzarotti 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
# Work Indexed citations
1 0
2 3
3 2
4 13
5 3
6 15
7 6
8 19
9 9
10 3
11 11
12 16
13 4
14 28
15 7
16
Eye localization: a survey
17
17 6
18 5
19 27
20 6

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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