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.
The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression
20102.8k citationsPatrick Lucey, Jeffrey F. Cohn et al.profile →
Painful data: The UNBC-McMaster shoulder pain expression archive database
2011408 citationsPatrick Lucey, Jeffrey F. Cohn et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Patrick Lucey'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 Patrick Lucey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick Lucey more than expected).
This network shows the impact of papers produced by Patrick Lucey. 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 Patrick Lucey. The network helps show where Patrick Lucey may publish in the future.
Co-authorship network of co-authors of Patrick Lucey
This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Lucey.
A scholar is included among the top collaborators of Patrick Lucey 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 Patrick Lucey. Patrick Lucey is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zheng, Stephan, et al.. (2018). Generating Multi-Agent Trajectories using Programmatic Weak Supervision. CaltechAUTHORS (California Institute of Technology).4 indexed citations
3.
Le, Hoang, Peter Carr, Yisong Yue, & Patrick Lucey. (2017). DATA-DRIVEN GHOSTING USING DEEP IMITATION LEARNING. CaltechAUTHORS (California Institute of Technology).23 indexed citations
Fernando, Tharindu, Xinyu Wei, Clinton Fookes, Sridha Sridharan, & Patrick Lucey. (2015). Discovering methods of scoring in soccer using tracking data. QUT ePrints (Queensland University of Technology).12 indexed citations
Lucey, Patrick, et al.. (2012). Characterizing multi-agent team behavior from partial team tracings: evidence from the english premier league. QUT ePrints (Queensland University of Technology). 2. 1387–1393.19 indexed citations
Kleinschmidt, Tristan, et al.. (2011). Can audio-visual speech recognition outperform acoustically enhanced speech recognition in automotive environment?. QUT ePrints (Queensland University of Technology).
Dean, David, et al.. (2007). Weighting and normalisation of synchronous HMMs for audio-visual speech recognition. QUT ePrints (Queensland University of Technology). 28.2 indexed citations
19.
Lucey, Patrick & Sridha Sridharan. (2006). Patch-Based Representation of Visual Speech. QUT ePrints (Queensland University of Technology). 79–85.4 indexed citations
20.
Dean, David, et al.. (2005). Audio-Visual Speaker Identification using the CUAVE Database. QUT ePrints (Queensland University of Technology). 97–102.1 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.