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+------------
+
+status: draft
+title: MegaFace
+desc: <span class="dataset-name">MegaFace</span> is a face recognition dataset created by scraping Flickr photo albums
+subdesc: MegaFace contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance
+slug: MegaFace
+cssclass: dataset
+image: assets/background.jpg
+year: 2007
+published: 2019-2-23
+updated: 2019-2-23
+authors: Adam Harvey
+
+------------
+
+## MegaFace Dataset
+
+### sidebar
+### end sidebar
+
+[ page under development ]
+
+*MegaFace (Viewpoint Invariant Pedestrian Recognition)* is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way."
+
+MegaFace is amongst the most widely used publicly available person re-identification datasets. In 2017 the MegaFace dataset was combined into a larger person re-identification created by the Chinese University of Hong Kong called PETA (PEdesTrian Attribute).
+
+{% include 'dashboard.html' %}
+
+
+### Research notes
+
+Dataset was used in research paper funded by SenseTime
+
+- https://verify.megapixels.cc/paper/megaface/verify/380d5138cadccc9b5b91c707ba0a9220b0f39271
+- x
+
+From "On Low-Resolution Face Recognition in the Wild:Comparisons and New Techniques"
+
+- Says 130,154 Flickr accounts, but I got 48,382
+- https://verify.megapixels.cc/paper/megaface/verify/841855205818d3a6d6f85ec17a22515f4f062882
+
+> 2) MegaFace Challenge 2 LR subset:The MegaFace challenge 2 (MF2) training dataset [48] is the largest (in the numberof identities) publicly available facial recognition dataset, with4.7 million face images and over 672,000 identities. The MF2dataset is obtained by running the Dlib [29] face detector onimages from Flickr [68], yielding 40 million unlabeled faces across 130,154 distinct Flickr accounts. Automatic identity labeling is performed using a clustering algorithm. We per-formed a subset selection from the MegaFace Challenge 2training set with tight bounding boxes to generate a LR subsetof this dataset. Faces smaller than 50x50 pixels are gathered for each identity, and then we eliminated identities with fewer thanfive images available. This subset selection approach produced 6,700 identities and 85,344 face images in total. The extractionprocess does yield some non-face images, as does the originaldataset processing. No further data cleaning is conducted onthis subset.
+
+UHDB31: A Dataset for Better Understanding Face Recognitionacross Pose and Illumination Variatio
+
+- http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Le_UHDB31_A_Dataset_ICCV_2017_paper.pdf
+- MegaFace 1 used 690,572 and 1,027,060
+- MegaFace 2 used 672,057 and 4,753,320
+\ No newline at end of file