Localizing parts of faces using a consensus of exemplars
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doi.org/10.1109/cvpr.2011.5995602 →Countries where authors are citing Localizing parts of faces using a consensus of exemplars
This map shows the geographic impact of Localizing parts of faces using a consensus of exemplars. 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 Localizing parts of faces using a consensus of exemplars with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Localizing parts of faces using a consensus of exemplars more than expected).
Fields of papers citing Localizing parts of faces using a consensus of exemplars
This network shows the impact of Localizing parts of faces using a consensus of exemplars. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Localizing parts of faces using a consensus of exemplars.
About Localizing parts of faces using a consensus of exemplars
This paper, published in 2011, received 420 indexed citations . Written by Peter N. Belhumeur, David W. C. Jacobs, David Kriegman and Neeraj Kumar covering the research area of Computer Vision and Pattern Recognition and Signal Processing. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (397 citations), Signal Processing (146 citations), Computational Mechanics (28 citations), Human-Computer Interaction (28 citations) and Experimental and Cognitive Psychology (19 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.
This paper is also available at doi.org/10.1109/cvpr.2011.5995602.