Cumulative Attribute Space for Age and Crowd Density Estimation

Cumulative Attribute Space for Age and Crowd Density Estimation Ke Chen1, ShaogangGong1, Tao Xiang1, Chen Change Loy2 1. Queen Mary, University of London 2. The Chinese University of Hong Kong CVPR 2013, Portland, Oregon

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

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

Challenge – Feature Variation The same age Feature • Extrinsic conditions: Lighting conditions; • Viewing angles • Intrinsic conditions: aging process of different people • glasses, hairstyle, gender, ethnicity

Challenge – Feature Variation The same person count Feature • Extrinsic conditions: Lighting conditions; • Viewing angles • Intrinsic conditions: occlusion, density distribution in the scene

Challenge – sparse and Imbalanced data Data distribution of FG-NET Dataset Max number of samples for each age group is 46

Challenge – sparse and Imbalanced data Data distribution of UCSD Dataset

Related Works • Most focused on feature variation challenge • Few focused on sparse and imbalanced data challenge • Two challenges are related Improve feature robustness [Guoet 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]

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

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

Limitation of non-cumulative Attribute 0 0 0 … … … 0 0 0 0 0 1 20th … 1 0 21st Age 20 0 … Age 21 Age 60 0 1 0 60th 0 0 0 … … … 0 0 0

Advantages of Cumulative Attribute 1 1 1 1 1 1 20 … … … 21 40 attributes change 1 attribute changes 1 60 1 1 1 0 1 Age 60 Age 21 Age 20 … … 0 0 1 the rest 0 0 0 … … … 0 0 0

Our framework 1 2 yiyi+1 N … … 1 1 0 0 1 Cumulative Attributes ai Multi-output Regression Learning Regression Mapping Facial images Crowd frames Feature Extraction Imagery Features xi Labels yi Regression Learning Conventional frameworks

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

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;

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;

Cumulative (CA) vs. non-cumulative (NCA) Age Estimation Crowd Counting

Robustness Against Sparse and Imbalanced Data Age Estimation Crowd Counting

Feature selection by Attributes Shape plays a more important role than texture when one is younger.

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

Thanks a lot for your attention! Any questions? Welcome to our poster 3A-2 for more details. Ke Chen Shaogang Gong Tao Xiang Chen Change Loy Ph.D student Professor Associate Professor Assistant Professor

Cumulative Attribute Space for Age and Crowd Density Estimation