|Table of Contents|

Age Estimation Based on Deep Learning MPCANet(PDF)


Research Field:
Publishing date:


Age Estimation Based on Deep Learning MPCANet
Zheng DepengDu JixiangZhai Chuanmin
School of Computer Science and Technology,Huaqiao University,Xiamen 361021,China
deep learningage estimationmulti principal component analysis network(MPCANet)
This paper investigates deep learning techniques for age estimation based on the multi principal component analysis network(MPCANet). A new framework for age feature extraction based on deep learning model with convolutional neural network(CNN)is built. The MPCANet is a variation of principal component analysis network(PCANet),which is recently proposes deep learning algorithms. The MPCANet model architecture components:(1)the use of Multi cascaded principal component analysis(PCA)in the convolution filter layer;(2)the nonlinear process layer by binary hashing; and(3)the use of block histogram in the feature pooling layer. We use K-SVR(Kernel function Support Vector Regression,K-SVR)for age estimation. Experimental results on two datasets(FG-NET and MORPH)show that the proposed approach is significantly better than the state-of-the-art.


[1] GENG X,ZHOU Z H,ZHANG Y,et al. Learning from facial aging patterns for automatic age estimation[C]//ACM International Conference on Multimedia. Santa Barbara,USA,2006:307-316.
[2]GUODONG G,YUN F,DYER C R,et al. Image-based human age estimation by manifold learning and locally adjusted robust regression.[J]. IEEE transactions on image processing,2008,17(7):1 178-1 188.
[3]LAWRENCE S,GILES C L,TSOI A C,et al. Face recognition:a convolutional neural-network approach[J]. IEEE transactions on neural networks,1997,8(1):98-113.
[4]KHALIL H M,SUNG L S. A convolutional neural network approach for face verification[C]//2014 International Conference on High Performance Computing and Simulation(HPCS). Bologna,Italy,2014:707-714.
[5]PATTABHI R N,IJJINA E P,MOHAN C K. Illumination invariant face recognition using convolutional neural networks[C]//IEEE International Conference on Signal Processing,Informatics,Communication and Energy Systems. Kozhikode,India:IEEE,2015:1-4.
[6]WANG X,GUO R,KAMBHAMETTU C. Deeply-learned feature for age estimation[C]//Applications of Computer Vision. Waikloloa,HI,USA:IEEE,2015:534-541.
[7]CHAN T H,JIA K,GAO S,et al. PCANet:a simple deep learning baseline for image classification[J]. IEEE transactions on image processing,2014,24(12):5 017-5 032.
[8]FUKAI H,TAKIMOTOY H,MITSUKURA Y. Age and gender estimation by using facial image[C]//Proceeding of the 11th IEEE International Workshop on Advances Motion Control. Nagaoka,Japan:IEEE Computer Society Press,2010:179-184.
[9]FU Y,HUANG T S. Human age estimation with regression on discriminative aging manifold[J]. IEEE transactions on multimedia,2008,10(4):578-584.
[10]ZHAI C M,YU Q,DU J X. Age estimation of facial images based on an improved non-negative matrix factorization algorithms[C]//Advanced Intelligent Computing Theories and Applications with Aspects of Artificial Intelligence,Proceedings of International Conference on Intelligent Computing,Icic 2010. Changsha,China,2010:1 865-1 868.
[11]RICANEK JR K,TESAFAYE T. MORPH:a longitudinal image database of normal adult age-progression[C]//IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. Southamptorm,UK,2006:341-345.
[12]CHANG K Y,CHEN C S,HUNG Y P. Ordinal hyperplanes ranker with cost sensitivities for age estimation[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs,USA:IEEE,2011:585-592.
[13]XIN G,ZHI H Z,KATE S M. Automatic age estimation based on facial aging patterns[J]. IEEE transactions on pattern analysis and machine intelligence,2007,29(12),2 234-2 240.
[14]GUO D G,YUN F,DYER C R,et al:Image-based human age estimation by manifold learning and locally adjusted robust regression[J]. IEEE transactions on image processing,2008,17(7):1 178-1 188.
[15]CHEN K,GONG S,XIANG T,et al. Cumulative attribute space for age and crowd density estimation[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Portland,USA:IEEE,2013:2 467-2 474.
[16]HADCHUM P,WONGTHANAVASU S. Facial age estimation using a hybrid of SVM and Fuzzy Logic[C]//2015 12th International Conference on Electrical Engineering/Electronics,Computer,Telecommunications and Information Technology(ECTI-CON). Hua Hin,Thailand:IEEE,2015:10-21.
[17]ZHENG D P,DU J X,FAN W T,et al. Deep learning with PCANet for human age estimation[M]//International Conference on Intelligent Computing. Lanzhou,China,2016:300-310.


Last Update: 1900-01-01