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FDDB: A Benchmark for Face Detection in Unconstrained Settings

Vidit jain. Erik learned-miller. FDDB: A Benchmark for Face Detection in Unconstrained Settings. http://vis-www.cs.umass.edu/lfw/index.html. Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts, Amherst . 2010. Aluna: Lourdes Ramírez Cerna. INTRODUCTION.

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FDDB: A Benchmark for Face Detection in Unconstrained Settings

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  1. Viditjain Erik learned-miller FDDB: A Benchmark for Face Detection in Unconstrained Settings http://vis-www.cs.umass.edu/lfw/index.html Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts, Amherst. 2010. Aluna: Lourdes Ramírez Cerna.

  2. INTRODUCTION Several algorithms have been developing for face detections, however remain difficult to compare due to the lack of enough detail to reproduce the published results. This paper presents a new data set of face images with more faces and more accurate annotations for face regions. Also, propose two rigorous and precise methods for evaluating the performance of face detection algorithms. Finally, reports results of several standard algorithms on the new benchmark. 2

  3. Facedetection data set • 2845 imageswith a total of 5171 faces. • The data set includesocclusions, difficult poses, lowresolutions and out-of-focus faces. • Especification of faceregions as ellipticalregions. • Bothgrayscale and color images. 3

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  6. Construction of the data set 6

  7. Near-duplicatedetection 7

  8. Annotatingfaceregions Forsomeimageregions, decidingwhether or not it represents a “face” can be challenging. Several factors such as low resolution (green, solid), occlusion (blue, dashed), and pose of the head (red, dotted) may make thisdeterminationambiguous. 8

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  10. Evaluation • A detection corresponds to a contiguous image region. • Any post-processing required to merge overlapping detections has already been done. • Each detection corresponds to exactly one entire face. 10

  11. Matching detections and annotations A matching of detections to face regions in this graph corresponds to the selection of a set of edges M ⊆ E. Mathematically, the desired matching M maximizes the cumulative matching score while satisfying the following constraints: The determination of the minimum weight matching in a weighted bipartite graph has an equivalent dual formulation as finding the solution of the minimum weighted (vertex) cover problem on a related graph. 11

  12. Evaluationmetrics • Discrete score: yi = S(di,vi)>0.5. A score of 1 is assigned to the detected region and 0 otherwise. • Continuous score: yi = S(di, vi). This ratio is used as the score for the detected region. 12

  13. Experimental Setup • 10 foldcross-validation A 10-fold cross-validation is performed using a fixed partitioning of the data set into ten folds. • Unrestricted training Data outside the FDDB data set is permitted to be included in the training set. 13

  14. Benchmark 14

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