Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster) while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

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

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

[3]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Panos J. Antsaklis,et al.  A simple method to derive bounds on the size and to train multilayer neural networks , 1991, IEEE Trans. Neural Networks.

[6]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[7]  Wei Zhao,et al.  Facial age estimation based on advanced ordinal ranking , 2015 .

[8]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

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

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

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

[13]  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).

[14]  Guang-Bin Huang,et al.  Learning to Rank with Extreme Learning Machine , 2013, Neural Processing Letters.

[15]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[16]  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.

[17]  Ye Xu,et al.  Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features , 2007, 2007 IEEE International Conference on Multimedia and Expo.

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

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

[20]  Yih-Fang Huang,et al.  Bounds on the number of hidden neurons in multilayer perceptrons , 1991, IEEE Trans. Neural Networks.

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

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

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

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

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

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

[27]  Guang-Bin Huang,et al.  Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.

[28]  Niels da Vitoria Lobo,et al.  Age Classification from Facial Images , 1999, Comput. Vis. Image Underst..

[29]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[30]  Yicong Zhou,et al.  Extreme learning machine for ranking: Generalization analysis and applications , 2014, Neural Networks.

[31]  C. Siew,et al.  Extreme Learning Machine with Randomly Assigned RBF Kernels , 2005 .

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

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