Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure

Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.

[1]  Luc Van Gool,et al.  Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks , 2016, International Journal of Computer Vision.

[2]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[3]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Guodong Guo,et al.  Human age estimation: What is the influence across race and gender? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[6]  Xiaolong Wang,et al.  Deeply-Learned Feature for Age Estimation , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[7]  Aki Vehtari,et al.  Gaussian process regression with Student-t likelihood , 2009, NIPS.

[8]  Kang Ryoung Park,et al.  Age estimation using a hierarchical classifier based on global and local facial features , 2011, Pattern Recognit..

[9]  Rama Chellappa,et al.  Modeling Age Progression in Young Faces , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

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

[12]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[15]  Xin Liu,et al.  AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[16]  Ming Yang,et al.  Correspondence driven adaptation for human profile recognition , 2011, CVPR 2011.

[17]  Xiaoming Liu,et al.  Demographic Estimation from Face Images: Human vs. Machine Performance , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Xiu-Shen Wei,et al.  Deep Label Distribution Learning for Apparent Age Estimation , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[19]  Ralph Gross,et al.  An Image Preprocessing Algorithm for Illumination Invariant Face Recognition , 2003, AVBPA.

[20]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Xiaogang Wang,et al.  Deep Convolutional Network Cascade for Facial Point Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[23]  Gang Hua,et al.  Ordinal Regression with Multiple Output CNN for Age Estimation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Yan Li,et al.  A Study on Apparent Age Estimation , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[25]  Stan Z. Li,et al.  Age Estimation by Multi-scale Convolutional Network , 2014, ACCV.

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

[27]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Meng Joo Er,et al.  Generalized Single-Hidden Layer Feedforward Networks for Regression Problems , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Meng Joo Er,et al.  Parsimonious Extreme Learning Machine Using Recursive Orthogonal Least Squares , 2014, IEEE Transactions on Neural Networks and Learning Systems.

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

[31]  Tal Hassner,et al.  Age and Gender Estimation of Unfiltered Faces , 2014, IEEE Transactions on Information Forensics and Security.

[32]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[33]  Eam Khwang Teoh,et al.  Facial age range estimation with extreme learning machines , 2015, Neurocomputing.

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

[35]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[36]  Jun Wan,et al.  DFDnet: Discriminant Face Descriptor Network for Facial Age Estimation , 2015, CCBR.

[37]  Luc Van Gool,et al.  DEX: Deep EXpectation of Apparent Age from a Single Image , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[38]  Tal Hassner,et al.  Age and gender classification using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[39]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[40]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

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

[42]  Sergio Escalera,et al.  ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[43]  Guodong Guo,et al.  Joint estimation of age, gender and ethnicity: CCA vs. PLS , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[44]  Karl Ricanek,et al.  MORPH: Development and Optimization of a Longitudinal Age Progression Database , 2009, COST 2101/2102 Conference.

[45]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[46]  Guodong Guo,et al.  Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression , 2011, CVPR 2011.

[47]  Luc Van Gool,et al.  Some Like It Hot — Visual Guidance for Preference Prediction , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).