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Face Recognition for Personal Photos using Online Social Network Context and Collaboration Guest Lecture at KAIST 14 December, 2010 Wesley De Neve,  Jaeyoung Choi, Yong Man Ro Image and Video Systems Lab Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Yuseong-gu, Daejeon, Republic of Korea e-mail:  [email_address] web:  http://ivylab.kaist.ac.kr
Professional Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Introduction (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Introduction (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/55
Face Detection, Recognition, and Annotation /55 face detection Identity tags: Barack Obama, Joe Biden face annotation retrieval of photos based on identity tags Barack Obama Joe Biden face recognition
Lecture Goals and Main Sources ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Application Areas of Face Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Conceptual Design of a Face Recognition System /55 preprocessing (e.g., scaling and rotation to put eyes on fixed locations) face detection gallery of known face images Hillary Joe Barack Robert matching unknown probe face images input photo ~ rank 1 rank 2 rank 3 rank 4 ranked list of candidate identities
[object Object],[object Object],[object Object],Matching Face Images /55 g a l l e r y p r o b e x = [  x 1 , ...,  x 72 ] feature extraction y = [  y 1 , ...,  y 72 ] feature extraction |x - y| n
Possible Outcomes of Face Recognition True negative (system correctly decides that the gallery does  not contain the identity of the probe face image) False negative (system incorrectly decides that the gallery does  not contain the identity of the probe face image) False positive (system incorrectly matches the probe face  image with one of the gallery face images) True positive (system correctly matches the probe face image  with one of the gallery face images) /55 = X = V = ? V = ? X min ( |x - y| n   )
Effectiveness of Face Recognition (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/55
Effectiveness of Face Recognition (2/2) ,[object Object],[object Object],[object Object],probe face image first hit at rank 12 rank 1 rank 2 ... /55 image from [2]
Room for Improvement... ,[object Object],[object Object],/55 (1) appearance-based face recognition   for personal photos (1) (2) appearance-based face recognition  for personal photos using   online social network context (2)
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Online Sharing of Personal Photos ,[object Object],[object Object],[object Object],[object Object],[object Object],/55 event photographer sharing
Online Social Network Context (1/2) ,[object Object],[object Object],/55 manually labeled face images contact list of the photographer (social network structure)
Online Social Network Context (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Empirical Study for Facebook ,[object Object],[object Object],[object Object],(*) using an open-source frontal face detector, it was found that 32% of the 8.1 million manually attached identity tags could be reliably associated with a machine-detectable frontal face /55 July 2009 Friends per volunteer (avg.) 645 Volunteers and friends (total #individuals) 22,108 Photos 7.7 million Identity tags (*) 8.1 million
Observations (1/2) ,[object Object],[object Object],significant amount of labeled face images that can be used to train and test face recognition algorithms /55 image from [2] Number of tags ( N ) Fraction of individuals tagged  N  times
Observations (2/2) ,[object Object],[object Object],[object Object],[object Object],/55
Summary Observations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Mathematical Modeling (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/55 image adopted from [2]
Mathematical Modeling (2/2) ,[object Object],[object Object],[object Object],[object Object],Markov Random Field (MRF) /55 y 1 y 2 y 3 φ 2 φ 1 φ 3 φ 1,2 φ 2,3 φ 1,3
Experimental Results (1/2) Probability  – proportion of face images with a correct label in the top R  suggested labels /55 image from [2] Rank ( R ) – number of suggested identity labels
Experimental Results (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Centralized and Decentralized Online Social Networks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55 image from The Economist
Architecture of Centralized and Decentralized Online Social Networks ,[object Object],[object Object],[object Object],[object Object],/55
Collaborative Face Recognition (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/55
Collaborative Face Recognition (2/2) /55 personal server photographer photos FR engine personal server contact 1 (family member) photos FR engine personal server contact 3 (friend) photos FR engine personal server contact 2 (family member) photos FR engine personal server contact 4 (co-worker) photos FR engine
Proposed Framework for Collaborative FR ,[object Object],[object Object],[object Object],[object Object],[object Object],/55 input photo face detection FR engine 2 FR engine 1 FR engine  K ... nametagged photo Mark Zuckerberg Jet Li fusion
Selection of Expert FR Engines using Online Social Network Context the thicker the line, the stronger the social tie, the more important the personalized FR engine of the corresponding contact /55 Weighted social graph model for the photographer Contact list contact 1 contact 2 contact 3 contact 4 contact 5 contact 6 Social graph model for the photographer occurrence probabilities co-occurrence probabilities Labeled face images
Experimental Data Collected for Cyworld ,[object Object],/55 ID Age Gender Contacts Years active Volunteer 1 28 Female 165 7 Volunteer 2 29 Male 118 4 Volunteer 3 30 Female 170 6 Volunteer 4 27 Male 84 8 ID Photos Photos with tagged individuals Individuals tagged Detected face images Volunteer 1 251,211 188,422 2,510 213,363 Volunteer 2 109,021 81,211 1,834 94,452 Volunteer 3 117,772 94,297 2,607 104,408 Volunteer 4 69,987 59,753 1,302 64,412
Observations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Distribution of FR Engine Relevance Values Relevance FR engine FR engine index (in decreasing order of relevance) experimental results for Volunteer 1, having 165 Cyworld contacts  /55 28 out of 166 appearance-based FR engines come with high relevance values (‘inner social circle’) head of the  distribution
FR Effectiveness of Selected FR Engines Number of FR engines used Number of correctly recognized face images the collaborative use of 28 out of 166 appearance-based FR engines results in a maximum number of correctly recognized face images experimental results for Volunteer 1, having 165 Cyworld contacts  number of correctly recognized face images when 28 FR engines are used /55 x
Experimental Results (1/2) Collaborative FR (Bayesian) Collaborative FR (Voting) Non-collaborative FR (Avg.) Rank ( R ) Probability (proportion of face images with a correct label in the top R  suggested labels) /55 experimental results for the 28 FR engines selected for Volunteer 1, for both collaborative and non-collaborative FR collaborative vs.  non-collaborative FR
Experimental Results (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Microsoft OneAlbum ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Augmented Identity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Socially-Aware Advertisement Billboards ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Socially-Aware Video Surveillance (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Socially-Aware Video Surveillance (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Socially-Aware Robots ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55 Albert Einstein Hugo
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
[object Object],[object Object],[object Object],/55
Video Demos ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/55
References (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/55
References (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/55
Picture Credits ,[object Object],[object Object],[object Object],[object Object],/55

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Face Recognition for Personal Photos using Online Social Network Context and Collaboration

  • 1. Face Recognition for Personal Photos using Online Social Network Context and Collaboration Guest Lecture at KAIST 14 December, 2010 Wesley De Neve, Jaeyoung Choi, Yong Man Ro Image and Video Systems Lab Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Yuseong-gu, Daejeon, Republic of Korea e-mail: [email_address] web: http://ivylab.kaist.ac.kr
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  • 7. Face Detection, Recognition, and Annotation /55 face detection Identity tags: Barack Obama, Joe Biden face annotation retrieval of photos based on identity tags Barack Obama Joe Biden face recognition
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  • 11. Conceptual Design of a Face Recognition System /55 preprocessing (e.g., scaling and rotation to put eyes on fixed locations) face detection gallery of known face images Hillary Joe Barack Robert matching unknown probe face images input photo ~ rank 1 rank 2 rank 3 rank 4 ranked list of candidate identities
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  • 13. Possible Outcomes of Face Recognition True negative (system correctly decides that the gallery does not contain the identity of the probe face image) False negative (system incorrectly decides that the gallery does not contain the identity of the probe face image) False positive (system incorrectly matches the probe face image with one of the gallery face images) True positive (system correctly matches the probe face image with one of the gallery face images) /55 = X = V = ? V = ? X min ( |x - y| n )
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  • 27. Experimental Results (1/2) Probability – proportion of face images with a correct label in the top R suggested labels /55 image from [2] Rank ( R ) – number of suggested identity labels
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  • 33. Collaborative Face Recognition (2/2) /55 personal server photographer photos FR engine personal server contact 1 (family member) photos FR engine personal server contact 3 (friend) photos FR engine personal server contact 2 (family member) photos FR engine personal server contact 4 (co-worker) photos FR engine
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  • 35. Selection of Expert FR Engines using Online Social Network Context the thicker the line, the stronger the social tie, the more important the personalized FR engine of the corresponding contact /55 Weighted social graph model for the photographer Contact list contact 1 contact 2 contact 3 contact 4 contact 5 contact 6 Social graph model for the photographer occurrence probabilities co-occurrence probabilities Labeled face images
  • 36.
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  • 38. Distribution of FR Engine Relevance Values Relevance FR engine FR engine index (in decreasing order of relevance) experimental results for Volunteer 1, having 165 Cyworld contacts /55 28 out of 166 appearance-based FR engines come with high relevance values (‘inner social circle’) head of the distribution
  • 39. FR Effectiveness of Selected FR Engines Number of FR engines used Number of correctly recognized face images the collaborative use of 28 out of 166 appearance-based FR engines results in a maximum number of correctly recognized face images experimental results for Volunteer 1, having 165 Cyworld contacts number of correctly recognized face images when 28 FR engines are used /55 x
  • 40. Experimental Results (1/2) Collaborative FR (Bayesian) Collaborative FR (Voting) Non-collaborative FR (Avg.) Rank ( R ) Probability (proportion of face images with a correct label in the top R suggested labels) /55 experimental results for the 28 FR engines selected for Volunteer 1, for both collaborative and non-collaborative FR collaborative vs. non-collaborative FR
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Notas del editor

  1. http://www.gartner.com/it/page.jsp?id=1447613
  2. http://www.economist.com/node/10880936?story_id=10880936 https://joindiaspora.com/ http://www.thimbl.net/
  3. http://ilabs.microsoft.com/Project/Pages/Project.aspx?ProjectId=4
  4. http://www.youtube.com/watch?v=tb0pMeg1UN0
  5. Screenshot from “Minority Report”.
  6. Pictures of “Albert Einstein Hubo”.