Face Association by Model Evolution: Learning people's face from weakly labelled web images. Just search the one in Goggle, eliminate irrelevant images automatically and train a classifier. My CVPR paper...

1. +
FAME:
Face Association
through Model
Evolution
Pinar Duygulu (CMU, Hacettepe University)
Eren Golge (Bilkent University)

2. +
Yale dataset:
10 subjects, 9 poses, 64 illuminations
Labelled Faces in the wild
13323 Faces of 5749 celebrities
PubFig dataset
60,000 images of 200 people
Social Face Classification
4.4 million labeled faces from
4,030 people

3. +
Labeling for how many?

4. +
Search web for faces of a query name

5. +
Use this set to learn models

6. +
Variations and sub-categories

7. +
Irrelevant people

8. +
Find category related images in
the set of weakly labeled images

9. +Our previous work: Densest component
Most similar set of faces as a subgraph
Assumption:
The most similar
subset of faces
among the faces
associated with a
name will be the
correct faces
Drawback:
Finds a single
subset
Ozkan, D., Duygulu, P., ”Interesting Faces: A Graph Based Approach for Finding People in News”, Pattern
Recognition, 2010
Ozkan, D., Duygulu, P., ”A Graph Based Approach for Naming Faces in News Photos”, CVPR, 2006
Ozkan, D., Duygulu, P., ”Finding People Frequently Appearing in News”, CIVR, 2006

10. +
Our previous work: Concept Maps
Grouping and outlier removal
Golge, E., Duygulu, P., “Concept Maps: Mining Noisy Web Data for Concept Learning ”, accepted

11. + Our previous work: Concept Maps
Grouping and outlier removal
Assumption:
Faces of a single
person can have sub-
categories
Outliers are different
than the queried
person
Drawback:
Eliminates strange
looks not in groups
as well

12. +
FAME
Face Association through Model Evolution
Capture discriminative and representative category
images through iterative data cleansing
Separate category instances versus random images.
Agnostic data refining method against Irrelevancy.
Evade Sub-Grouping using very high dimensional
representations.

13. + Overview of FAME
First discern category candidates (CC) from random set (RS).
Define category references(CR) inside CC .
Second discern CR from CC.
Define spurious instances (SI) against CR and eliminate.
Re-Iterate

14. +
Step 1
Discerning category from random set
Learn a linear model M1 betweencategory
candidates CC and random set RS.
Take the most confidently classified instances
as the category references CR.

15. +
Step 2
Discerning category references from others
Another model M2 between category references
CR and other category candidates.

16. +
Step 3
Define spurious instances SI against category references CR.
Eliminate SI.

17. + FAME

18. +
Eliminations by iteration

19. +
Eliminations by iteration

20. +
High Dimensional Representation
High dimensions help a category linearly separable
from others despite of category modularity.

21. +
Feature learning
Coates, Adam, Andrew Y. Ng, and Honglak Lee. "An analysis of single-layer networks in unsupervised feature
learning." International Conference on Artificial Intelligence and Statistics. 2011.

22. +
Raw pixel LBP encoded outliers

23. +
Implementation details
FAME
Data refining : L1 Logistic Regression with Gauss-Seidel algorithm [1]
Final Classifier: L1 Linear SVM with Grafting[2].
At each iteration 5 images are eliminated.
Feature Learning
Augment train data with horizontally flipped images.
Re-size each gray-level image 60px height.
Contrast Normalization to random patches.
ZCA whitening with Ɛ=0.5.
Receptive field (patch) size 6x6 pixels
1 pixel stride with k=2400 words.
Final feature vector has 5x2400 dimensions.
[1] Shirish Krishnaj Shevade and S Sathiya Keerthi. A simple and efficient algorithm for gene selection using sparse logistic
regression. Bioinformatics,19(17):2246–2253, 2003.
[2] Simon Perkins, Kevin Lacker, and JFAMEs Theiler. Grafting: Fast, incremental feature selection by gradient descent in
function space. The Journal of Machine Learning Research, 3:1333–1356, 2003.

24. +
Datasets
PubFig83
Subset of PubFig with 83 celebrities
at least 100 images for each.
N. Pinto, Z. Stone, T. Zickler, and D. Cox, “Scaling up biologically-inspired computer vision: A case
study in unconstrained face recognition on facebook,” in Computer Vision and Pattern
Recognition Workshops (CVPRW), 2011.

25. +
Datasets
FAN-Large
EASY subset: faces larger than 60x70 px, 138 categories.
ALL: no constraint, 365 categories.
M. Ozcan, J. Luo, V. Ferrari, and B. Caputo, “A large-scale database of images and captions for
automatic face naming.,” in BMVC, pp. 1–11, 2011.

26. + Results on PubFig83
N. Pinto, Z. Stone, T. Zickler, and D. Cox, “Scaling up biologically-inspired computer vision: A case study in
unconstrained face recognition on facebook,” in Computer Vision and Pattern Recognition Workshops
(CVPRW), 2011 IEEE Computer Society Conference on, pp. 35–42, IEEE, 2011
B. C. Becker and E. G. Ortiz, “Evaluating open-universe face identification on the web,” in Computer Vision
and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on, pp. 904–911, IEEE, 2013.
No data refining, only our classification pipeline.
Models are trained on the training set of the given dataset
~5% improvement on State of Art

27. +
Evaluations

28. + False versus true outlier elimination

29. +
Cross validation accuracies

30. +
Number of eliminations versus accuracy

31. +Models learned from weakly labeled set
 Baseline: all images collected for the query are used
 AME-M1 : Only M1 classifier which removes against global
negatives
 AME-SVM : with SVM as the final classifier
 AME-LR : the proposed method
S. Singh, A. Gupta, and A. A. Efros, “Unsupervised discovery of mid-level discriminative patches,”
in European Conference Computer Vision (ECCV), 2012.

32. +
Summary
 A method to build training sets from weakly-
labeled images
 Iterative pruning removes the outliers which are
the least confident instances
 High dimensional feature representation handles
the variations

33. +
TUBITAK 112E174
CHIST-ERA MUCKE
US Department of Defense, U. S. Army Research Office (W911NF-13-1-0277)
National Science Foundation Grant No. IIS-1251187
Thanks

34. +
Use annotated control set as a start point.
Fergus et. al. [1], OPTIMOL, Li and Fei-Fei [2]
We use fully autonomous framework.
Use Textual Captions
Berg and Forsyth [3]
We use only visual content
Discriminative image cues
Efros et al. [4] “Discriminative Patches”, Q. Li et al.[5]
We use single computer with faster and better results.
[1] Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from google’s image
search. In: Computer Vision, 2005. ICCV 2005
[2] Berg, T.L., Berg, A.C., Edwards, J., Maire, M., White, R., Teh, Y.W., Learned-Miller, E.G., Forsyth,
D.A.: NFAMEs and faces in the news. In: IEEE Conference on Computer Vision
Pattern Recognition (CVPR). Volume 2. (2004) 848–854
[3] Li, L.J., Fei-Fei, L.: Optimol: automatic online picture collection via incremental model learning.
International journal of computer vision 88(2) (2010) 147–168
[4] Li, Q., Wu, J., & Tu, Z. Harvesting Mid-level Visual Concepts from Large-scale Internet Images.