Strategic Decision-Making Learning from Label Distributions: An Approach for Facial Age Estimation

Nowadays, label distribution learning is among the state-of-the-art methodologies in facial age estimation. It takes the age of each facial image instance as a label distribution with a series of age labels rather than the single chronological age label that is commonly used. However, this methodology is deficient in its simple decision-making criterion: the final predicted age is only selected at the one with maximum description degree. In many cases, different age labels may have very similar description degrees. Consequently, blindly deciding the estimated age by virtue of the highest description degree would miss or neglect other valuable age labels that may contribute a lot to the final predicted age. In this paper, we propose a strategic decision-making label distribution learning algorithm (SDM-LDL) with a series of strategies specialized for different types of age label distribution. Experimental results from the most popular aging face database, FG-NET, show the superiority and validity of all the proposed strategic decision-making learning algorithms over the existing label distribution learning and other single-label learning algorithms for facial age estimation. The inner properties of SDM-LDL are further explored with more advantages.

[1]  Yun Fu,et al.  Human age estimation using bio-inspired features , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Jian-Jiun Ding,et al.  Facial age estimation based on label-sensitive learning and age-oriented regression , 2013, Pattern Recognit..

[3]  K. Shadan,et al.  Available online: , 2012 .

[4]  Chia-Wen Lin,et al.  Bayesian age estimation on face images , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[5]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[6]  Jiwen Lu,et al.  Multi-feature ordinal ranking for facial age estimation , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[7]  Yun Fu,et al.  Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression , 2008, IEEE Transactions on Image Processing.

[8]  Hongyuan Zha,et al.  Learning distance metric for regression by semidefinite programming with application to human age estimation , 2009, ACM Multimedia.

[9]  Rama Chellappa,et al.  A hierarchical approach for human age estimation , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Rama Chellappa,et al.  Age Estimation and Face Verification Across Aging Using Landmarks , 2012, IEEE Transactions on Information Forensics and Security.

[11]  Yun Fu,et al.  Human Age Estimation With Regression on Discriminative Aging Manifold , 2008, IEEE Transactions on Multimedia.

[12]  C. Christodoulou,et al.  Comparing different classifiers for automatic age estimation , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Haizhou Ai,et al.  Demographic Classification with Local Binary Patterns , 2007, ICB.

[14]  Yi-Ping Hung,et al.  Ordinal hyperplanes ranker with cost sensitivities for age estimation , 2011, CVPR 2011.

[15]  Dit-Yan Yeung,et al.  Multi-task warped Gaussian process for personalized age estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Rama Chellappa,et al.  Face Verification Across Age Progression , 2006, IEEE Transactions on Image Processing.

[17]  Vasif V. Nabiyev,et al.  Age Estimation Based on Local Radon Features of Facial Images , 2012, ISCIS.

[18]  Li Zhang,et al.  A New Method for Age Estimation from Facial Images by Hierarchical Model , 2013, ICCC.

[19]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[20]  Zhi-Hua Zhou,et al.  Facial Age Estimation by Learning from Label Distributions , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  V. Sudhakar,et al.  Electronic Customer Relationship Management (E-CRM) , 2009 .

[22]  Yuyu Liang,et al.  A Hierarchical Framework for Facial Age Estimation , 2014 .

[23]  Thomas S. Huang,et al.  Metric Learning for Regression Problems and Human Age Estimation , 2009, PCM.

[24]  Bingbing Ni,et al.  Web image mining towards universal age estimator , 2009, ACM Multimedia.

[25]  Dimitris N. Metaxas,et al.  Ranking Model for Facial Age Estimation , 2010, 2010 20th International Conference on Pattern Recognition.

[26]  W. Buxton.  Human-Computer Interaction , 1988, Springer Berlin Heidelberg.

[27]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[28]  Shuicheng Yan,et al.  Synchronized Submanifold Embedding for Person-Independent Pose Estimation and Beyond , 2009, IEEE Transactions on Image Processing.

[29]  Shuicheng Yan,et al.  Ranking with Uncertain Labels , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[30]  Shuicheng Yan,et al.  Learning Auto-Structured Regressor from Uncertain Nonnegative Labels , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[31]  Shaogang Gong,et al.  Cumulative Attribute Space for Age and Crowd Density Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  A. Albert,et al.  A review of the literature on the aging adult skull and face: implications for forensic science research and applications. , 2007, Forensic science international.

[33]  Yun Fu,et al.  Locally Adjusted Robust Regression for Human Age Estimation , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[34]  Zhi-Hua Zhou,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Xin Geng,et al.  Label Distribution Learning , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[36]  Matti Pietikäinen,et al.  Age Estimation Using Local Binary Pattern Kernel Density Estimate , 2013, ICIAP.