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authorJules Laplace <julescarbon@gmail.com>2018-10-31 02:15:42 +0100
committerJules Laplace <julescarbon@gmail.com>2018-10-31 02:15:42 +0100
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tree189c6f52c347cad780aba982c04efb8668eaa57f /datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv
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+Fddb: A benchmark for face detection in unconstrained settings|http://www.cs.umass.edu/~elm/papers/fddb.pdf|2010|525|13|17267836250801810690|http://www.cs.umass.edu/~elm/papers/fddb.pdf|http://scholar.google.com/scholar?cites=17267836250801810690&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17267836250801810690&hl=en&as_sdt=0,5|None|Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two …