FDDB: A benchmark for face detection in unconstrained settings
- Authors
- Vidit JainErik Learned-Miller
- Journal
- ScholarWorks@UMassAmherst (University of Massachusetts Amherst)
In The Last Decade
doi.org/w31933888 →Countries where authors are citing FDDB: A benchmark for face detection in unconstrained settings
This map shows the geographic impact of FDDB: A benchmark for face detection in unconstrained settings. 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 FDDB: A benchmark for face detection in unconstrained settings with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites FDDB: A benchmark for face detection in unconstrained settings more than expected).
Fields of papers citing FDDB: A benchmark for face detection in unconstrained settings
This network shows the impact of FDDB: A benchmark for face detection in unconstrained settings. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the FDDB: A benchmark for face detection in unconstrained settings.
About FDDB: A benchmark for face detection in unconstrained settings
This paper, published in 2010, received 558 indexed citations . Written by Vidit Jain and Erik Learned-Miller covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (510 citations), Signal Processing (122 citations) and Artificial Intelligence (53 citations). Published in ScholarWorks@UMassAmherst (University of Massachusetts Amherst).
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/w31933888.