Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Face aging with conditional generative adversarial networks
Countries citing papers authored by Jean‐Luc Dugelay
Since
Specialization
Citations
This map shows the geographic impact of Jean‐Luc Dugelay'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 Jean‐Luc Dugelay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean‐Luc Dugelay more than expected).
Fields of papers citing papers by Jean‐Luc Dugelay
This network shows the impact of papers produced by Jean‐Luc Dugelay. 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 Jean‐Luc Dugelay. The network helps show where Jean‐Luc Dugelay may publish in the future.
Co-authorship network of co-authors of Jean‐Luc Dugelay
This figure shows the co-authorship network connecting the top 25 collaborators of Jean‐Luc Dugelay.
A scholar is included among the top collaborators of Jean‐Luc Dugelay 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 Jean‐Luc Dugelay. Jean‐Luc Dugelay is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Badii, Atta, et al.. (2015). Overview of the MediaEval 2015 Drone Protect Task. MediaEval.2 indexed citations
8.
Fradi, Hajer, et al.. (2014). Privacy Protection Filter Using Shape and Color Cues. Graduate School and Research Center in Digital Science (EURECOM).4 indexed citations
Dugelay, Jean‐Luc, et al.. (2013). Shape and Color-aware Privacy Protection. Graduate School and Research Center in Digital Science (EURECOM).1 indexed citations
Fradi, Hajer & Jean‐Luc Dugelay. (2012). People counting system in crowded scenes based on feature regression. European Signal Processing Conference. 136–140.16 indexed citations
13.
Zhao, Xu-Ran, Nicholas Evans, & Jean‐Luc Dugelay. (2012). Multi-view semi-supervised discriminant analysis: A new approach to audio-visual person recognition. Graduate School and Research Center in Digital Science (EURECOM). 31–35.5 indexed citations
14.
Dugelay, Jean‐Luc, et al.. (2012). MediaEval 2012: Scrambling Faces for Flexible Privacy Preservation using Image Background Self-similarities. Graduate School and Research Center in Digital Science (EURECOM).1 indexed citations
15.
Zhao, Xu-Ran, Nicholas Evans, & Jean‐Luc Dugelay. (2011). A co-training approach to automatic face recognition. Graduate School and Research Center in Digital Science (EURECOM). 1979–1983.5 indexed citations
16.
Doërr, Gwenaël & Jean‐Luc Dugelay. (2005). Problématique de la Collusion en Tatouage Vidéo. UCL Discovery (University College London).
17.
Doërr, Gwenaël, et al.. (2004). Exploiting self-similarities to defeat digital watermarking systems: a case study on still images. UCL Discovery (University College London).1 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.