1. PRESENTED BY KE CHEN DEPARTMENT OF SIGNAL PROCESSING TAMPERE UNIVERSITY OF TECHNOLOGY, FINLAND CUMULATIVE ATTRIBUTE SPACE FOR AGE AND CROWD DENSITY ESTIMATION Valse 2014-04-01

2. PROBLEMS How old are they? How many persons are in the scene? What is the head pose (viewing angles) of this person?

3. A REGRESSION FORMULATION Original images/frames Facial images Crowd frames AAM feature Segment feature Edge feature Texture feature Feature extraction Feature space Label space Labels Learning the mapping Regression

4. CHALLENGE – FEATURE VARIATION The same age Extrinsic conditions: Lighting conditions; Viewing angles Intrinsic conditions: aging process of different people glasses, hairstyle, gender, ethnicity Feature

5. CHALLENGE – FEATURE VARIATION The same person count Extrinsic conditions: Lighting conditions; Viewing angles Intrinsic conditions: occlusion, density distribution in the scene Feature

6. CHALLENGE – SPARSE AND IMBALANCED DATA Data distribution of FG-NET Dataset Max number of samples for each age group is 46

7. CHALLENGE – SPARSE AND IMBALANCED DATA Data distribution of UCSD Dataset

8. RELATED WORKS Most focused on feature variation challenge Few focused on sparse and imbalanced data challenge Two challenges are related 1.Improve feature robustness [Guo et al, CVPR, 2009; Guo et al, TIP, 2012; Ryan et al, DICTA, 2009; Zhang et al, IEEE T ITS, 2011]. 2. Improve regressor [Guo et al, TIP 2008; Chang et al, CVPR 2011; Chao et al, PR 2013; Chan et al, CVPR 2008; Chen et al, BMVC 2012]

9. OUR APPROACH Solution: Attribute Learning can address data sparsity problem -- Exploits the shared characteristics between classes Has sematic meaning Discriminative Problems: Applied successfully in classification but not in regression How to exploit cumulative dependent nature of labels in regression? …… …… …… Age 20 Age 21 Age 60

10. CUMULATIVE ATTRIBUTE Age 20 1 1 0 1 … 20 0 … 0 the rest Cumulative attribute (dependent) Vs. 0 1 … 20th 0 … 0 Non-cumulative attribute (independent) 0 0

11. LIMITATION OF NON-CUMULATIVE ATTRIBUTE Age 20 0 1 … 20th 0 … 0 Age 60 60th 0 … 0 0 0 1 … 0 … 0 0 … 0 0 21st 0 1 … 0 … 0 0 0 0 Age 21

12. ADVANTAGES OF CUMULATIVE ATTRIBUTE Age 20 1 1 0 1 … 20 0 … 0 the rest Age 60 1 1 1 … 60 0 … 0 1 0 … 1 … 1 attribute changes 1 1 … 21 0 … 0 1 1 0 40 attributes change

13. OUR FRAMEWORK Imagery Features x i Facial images Crowd frames Labels y i Regression Learning Cumulative Attributes a i Feature Extraction Multi-output Regression Learning Regression Mapping Conventional frameworks 1100 … … 1 1 2 y i y i +1 N

14. JOINT ATTRIBUTE LEARNING Joint Attribute Learning with quadratic loss function Regression Learning with attribute representation as input is not limited to a specific regression model

15. COMPARATIVE EVALUATION Age Estimation CA-SVR: our method; AGES: Geng et al, TPAMI, 2007; RUN: Yan et al, ICCV, 2007; Ranking: Yan et al, ICME, 2007; RED-SVM: Chang et al, ICPR, 2010; LARR: Guo et al, TIP, 2008; MTWGP: Zhang et al, CVPR, 2010; OHRank: Chang et al, CVPR, 2011; SVR: Guo et al, TIP, 2008;

16. COMPARATIVE EVALUATION Crowd Counting CA-RR: our method; LSSVR: Suykens et al, IJCNN, 2001; KRR: An et al, CVPR, 2007; RFR: Liaw et al, R News, 2002; GPR: Chan et al, CVPR, 2008; RR: Chen et al, BMVC, 2012;

17. CUMULATIVE (CA) VS. NON- CUMULATIVE (NCA) Crowd Counting Age Estimation

18. ROBUSTNESS AGAINST SPARSE AND IMBALANCED DATA Age Estimation Crowd Counting

19. FEATURE SELECTION BY ATTRIBUTES Shape plays a more important role than texture when one is younger.

20. CONCLUSION A novel attribute framework for regression Exploits cumulative dependent nature of label space Effectively addresses sparse and imbalanced data problem