Daniel Riccio

4.0k citations
69 papers · 2.0k indexed · 1 hit paper · h-index 22

Daniel Riccio

69 papers receiving 1.8k citations

Hit Papers

2D and 3D face recognition: A survey5702007202620132019100200300400500

Peers

Daniel Riccio
Comparison fields: 5 of 125
  • Signal Processing 969
  • Computer Vision and Pattern Recognition 1.4k
  • Human-Computer Interaction 158
  • Information Systems 389
  • Media Technology 89
Replace Jean‐Luc Dugelay with:
Jean‐Luc Dugelay France
Peter Peer Slovenia
Vitomir Štruc Slovenia
Eric Sung Singapore
Raymond Veldhuis Netherlands
Raghavendra Ramachandra Norway
Mássimo Tistarelli Italy
Adams Wai‐Kin Kong Singapore
Jianjiang Feng China
Rubén Vera-Rodríguez Spain
Daniel Riccio relative to Jean‐Luc Dugelay France Jean‐Luc Dugelay's profile →
Citations per field
00.5×1.6×
Jean‐Luc Dugelay · 1×
Citations per year

Countries citing papers authored by Daniel Riccio

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Riccio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Daniel Riccio, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Riccio Line = papers co-authored together Daniel Riccio links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202311
2 202265
3 202120
4
Hierarchical Cell-to-Tissue Graph Representations for Breast Cancer Subtyping in Digital Pathology.
20213
5 20207
6 201958
7 201829
8 20186
9 201727
10 201643
11 201530
12 20152
13 20155
14 201376
15 201281
16 201222
17 20117
18 20114
19 20104
20 200563

About Daniel Riccio

Daniel Riccio is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Space and Planetary Science, Ophthalmology and Information Systems, having authored 69 papers that have together received 2.0k indexed citations. Recurring topics across this work include Biometric Identification and Security (36 papers), Face recognition and analysis (28 papers), Face and Expression Recognition (24 papers), User Authentication and Security Systems (16 papers), Glaucoma and retinal disorders (6 papers), Retinal Imaging and Analysis (6 papers), AI in cancer detection (5 papers) and Digital Imaging for Blood Diseases (4 papers). The work is most often cited by research in Signal Processing (969 citations), Computer Vision and Pattern Recognition (1.4k citations), Human-Computer Interaction (158 citations), Information Systems (389 citations) and Media Technology (89 citations). Daniel Riccio has collaborated with scholars based in Italy, United States and France. Frequent co-authors include Michele Nappi, Maria De Marsico, Andrea F. Abate, Gabriele Sabatino, Harry Wechsler, Maria Frucci, Chiara Galdi, Nadia Brancati, Jean‐Luc Dugelay and Giuseppe De Pietro. Their work appears in journals such as Pattern Recognition Letters, Pattern Recognition, IEEE Access, Journal of Visual Languages & Computing and Journal of Ambient Intelligence and Humanized Computing.

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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