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authoradamhrv <adam@ahprojects.com>2019-10-08 10:15:36 +0200
committeradamhrv <adam@ahprojects.com>2019-10-08 10:15:36 +0200
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------------
-status: draft
+status: published
title: HELEN
-desc: HELEN Face Dataset
-subdesc: HELEN (under development)
+desc: HELEN is a dataset of face images from Flickr used for training facial component localization algorithms
+subdesc: HELEN includes 2,330 images from Flickr found by keyword searches for "portrait", "wedding", "outdoor", "boy", "studio", and "family"
+caption: Selected images from the HELEN dataset
slug: helen
cssclass: dataset
+caption: Example images from the HELEN dataset
image: assets/background.jpg
-year: 2000
-published: 2019-4-18
-updated: 2019-4-18
+published: 2019-9-23
+updated: 2019-9-23
authors: Adam Harvey
------------
-## HELEN
+
+# HELEN Dataset
### sidebar
### end sidebar
-[ page under development ]
+Helen is a dataset of annotated face images used for facial component localization. It includes 2,330 images from Flickr found by searching for "portrait" combined with terms such as "family", "wedding", "boy", "outdoor", and "studio".[^orig_paper]
+
+The dataset was published in 2012 with the primary motivation listed as facilitating "high quality editing of portraits". However, the paper's introduction also mentions that facial feature localization "is an essential component for face recognition, tracking and expression analysis."[^orig_paper]
+
+Irregardless of the authors' primary motivations, the HELEN dataset has become one of the most widely used datasets for training facial landmark algorithms, which are essential parts of most facial recogntion processing systems. Facial landmarking are used to isolate facial features such as the eyes, nose, jawline, and mouth in order to align faces to match a templated pose.
+
+![caption: An example annotation from the HELEN dataset showing 194 points that were originally annotated by Mechanical Turk workers. Graphic &copy; 2019 MegaPixels.cc based on data from HELEN dataset by Le, Vuong et al.](assets/montage_lms_21_14_14_14_26.png)
+
+This analysis shows that since its initial publication in 2012, the HELEN dataset has been used in over 200 research projects related to facial recognition with the vast majority of research taking place in China.
+
+Commercial use includes IBM, NVIDIA, NEC, Microsoft Research Asia, Google, Megvii, Microsoft, Intel, Daimler, Tencent, Baidu, Adobe, Facebook
+
+Military and Defense Usage includes NUDT
+
+http://eccv2012.unifi.it/
+
+TODO
+
+- add proof of use in dlib and openface
+- add proof of use in commercial use of dlib? ibm dif
+- make landmark over blurred images
+- add 6x6 gride for landmarks
+- highlight key findings
+- highlight key commercial usage
+- look for most interesting research papers to provide example of how it's used for face recognition
+- estimated time: 6 hours
+- add data to github repo?
+
+| Organization | Paper | Link | Year | Used Duke MTMC |
+|---|---|---|---|
+| SenseTime, Amazon | [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://arxiv.org/pdf/1805.10483.pdf)
+ | 2018 | year | &#x2714; |
+| SenseTime | [ReenactGAN: Learning to Reenact Faces via Boundary Transfer](https://arxiv.org/pdf/1807.11079.pdf) | 2018 | year | &#x2714; |
+
+
+The dataset was used for training the OpenFace software "we used the HELEN and LFPW training subsets for training and the rest for testing" https://github.com/TadasBaltrusaitis/OpenFace/wiki/Datasets
+
+The popular dlib facial landmark detector was trained using HELEN
+
+In addition to the 200+ verified citations, the HELEN dataset was used for
+- https://github.com/memoiry/face-alignment
+- http://www.dsp.toronto.edu/projects/face_analysis/
+
+It's been converted into new datasets including
+- https://github.com/JPlin/Relabeled-HELEN-Dataset
+- https://www.kaggle.com/kmader/helen-eye-dataset
+
+The original site
+- http://www.ifp.illinois.edu/~vuongle2/helen/
+
+### Example Images
+
+
+
+![caption: An image from the HELEN dataset "wedding" category used for training face recognition 2839127417_1.jpg for outdoor studio](assets/feature_outdoor_02.jpg)
+![caption: An image from the HELEN dataset "wedding" category used for training face recognition 2325274893_1 ](assets/feature_graduation.jpg)
+
+![caption: An image from the HELEN dataset "wedding" category used for training face recognition 2325274893_1 ](assets/feature_wedding.jpg)
+![caption: An image from the HELEN dataset "wedding" category used for training face recognition 2325274893_1 ](assets/feature_wedding_02.jpg)
+
+![caption: Original Flickr image used in HELEN facial analysis and recognition dataset for the keyword "family". 296814969](assets/feature_family.jpg)
+![caption: Original Flickr image used in HELEN facial analysis and recognition dataset for the keyword "family". 296814969](assets/feature_family_05.jpg)
+
{% include 'dashboard.html' %}
{% include 'supplementary_header.html' %}
+### Age and Gender Distribution
+
+{% include 'age_gender_disclaimer.html' %}
+
+=== columns 2
+
+```
+single_pie_chart /datasets/helen/assets/age.csv
+Caption: HELEN dataset age distribution
+Top: 10
+OtherLabel: Other
+```
+
+```
+single_pie_chart /datasets/helen/assets/gender.csv
+Caption: HELEN dataset gender distribution
+Top: 10
+OtherLabel: Other
+```
+
+=== end columns
+
+![caption: Visualization of the HELEN dataset 194-point facial landmark annotations. Credit: graphic &copy; MegaPixels.cc 2019, data from HELEN dataset by Zhou, Brand, Lin 2013. If you use this image please credit both the graphic and data source.](assets/montage_lms_21_15_15_7_26_0.png)
+
{% include 'cite_our_work.html' %}
+
+#### Cite the Original Author's Work
+
+If you find the HELEN dataset useful or reference it in your work, please cite the author's original work as:
+
+<pre>
+@inproceedings{Le2012InteractiveFF,
+ title={Interactive Facial Feature Localization},
+ author={Vuong Le and Jonathan Brandt and Zhe L. Lin and Lubomir D. Bourdev and Thomas S. Huang},
+ booktitle={ECCV},
+ year={2012}
+}
+</pre>
+
### Footnotes
+
+[^orig_paper]: Le, Vuong et al. “Interactive Facial Feature Localization.” ECCV (2012). \ No newline at end of file