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![Page 1: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/1.jpg)
Crowdsourcing Game Developmentfor Collecting Benchmark Data of Facial Expression Recognition Systems
Department of Information and Learning Technology National University of Tainan, Taiwan
Hsin-Chih Lin, Zi-Jie Li and Wan-Ling Chu
![Page 2: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/2.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 2
Outline
Introduction
Literature review
Crowdsourcing Game Development
Experimental Design and Results
01
02
03
04
Conclusions and Future Works05
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Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 3
Introduction
• Developing an automatic expression recognition system– always use benchmarks
• Most of facial pictures in benchmarks – not be accepted by the public or other teams
• Manually classifying facial expression pictures – labor-expensive– time-consuming– difficult to standardize
![Page 4: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/4.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 4
Literature review
• Crowdsourcing was first proposed by Howe (2006).
• The concept of crowdsourcing– to rely on manpower to complete the work
– difficult to be replaced by computer programs
• Microtask & National Library of Finland– Mole Bridge
– Mole Hunt
![Page 5: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/5.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 5
Literature review
• Von Ahn (2006) proposed the concept of
“Games with a Purpose”
– attract online players through interactive games
• “Gamification” can make boring becomes
interesting (Krause & Smeddinck, 2011).
• Listen Game(Turnbull et al., 2007)
– improve the results of music search
![Page 6: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/6.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 6
Crowdsourcing Game Development
LowValidity
Database
HighValidity
Database
Benchmark
FaceDetection
Crowdsourcing Game
FeatureExtraction
Classification
Face pictures
Social classification system
social = automatic
Automatic recognition system
social ≠ automatic
expression pictures of low validity
expression pictures of high validity
![Page 7: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/7.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 7
Crowdsourcing Game Development
• 3 by 3 grid– seven pictures– expression hint – two options
• Game-play rules– two minutes– randomly prompt
an expression hint
– none of the above
– skip
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Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 8
Experimental Design and Results
• This study enables crowds to classify facial expressions in the game during four-week experiments period– 100 participants – 1,416 times
• Training and testing method of the automatic expression recognition system : – 80/20– Incremental training
![Page 9: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/9.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 9
Experimental Design and Results
![Page 10: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/10.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 10
Experimental Design and Results
![Page 11: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/11.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 11
Experimental Design and Results
• Our study can effectively train automatic recognition system that allows the precision rate of system raised to extremely high in four-week testing.
• The dual system is able to develop an automatic recognition system in this study.
![Page 12: Crowdsourcing Game Development for Collecting Benchmark Data of Facial Expression Recognition Systems Department of Information and Learning Technology.](https://reader036.fdocuments.us/reader036/viewer/2022070412/56649e2c5503460f94b1ba79/html5/thumbnails/12.jpg)
Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 12
Experimental Design and Results
• Our benchmark– 84 happiness – 51 sadness – 34 surprise – 30 anger
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Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 13
Conclusions and Future Works
• An innovative dual system mechanism – an organism– enhanced the extremely high precision rate of
an automatic expression recognition system– efficiency and automation to classification that
no matter how many facial expression data needs to be classified
– resolve image classification or other issues must through human computation
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Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 14
Conclusions and Future Works
• Crowdsourcing Game– boring become interesting– save more time and cost– get the classification results agree with crowds
• Future Works– increase facial pictures– increase expressions categories(disgust, fear,
nature)
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Pacific Neighborhood Consortium Annual Conference and Joint Meetings 2013 15
Thank you for your attention.