Face Age Estimation Approach based on Deep Learning and Principle Component Analysis

This paper presents an approach for age estimation based on faces through classifying facial images into predefined age-groups. However, a task such as the one at hand faces several difficulties because of the different aspects of every single person. Factors like exposure, weather, gender and lifestyle all come into play. While some trends are similar for faces from a similar age group, it is problematic to distinguish the aging aspects for every age group. This paper’s concentration is in four chosen age groups where the estimation takes place. We employed a fast and effective machine learning method: deep learning so that it could solve the age categorization issue. Principal component analysis (PCA) was used for extracting features and reducing face image. Age estimation was applied to three different aging datasets from Morph and experimental results are reported to validate its efficiency and robustness. Eventually, it is evident from the results that the current approach has achieved high classification results compared with support vector machine (SVM) and k-nearest neighbors (K-NN).

[1]  Nicolas Le Roux,et al.  Deep Belief Networks Are Compact Universal Approximators , 2010, Neural Computation.

[2]  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.

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

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

[5]  Yi-Ping Hung,et al.  2010 International Conference on Pattern Recognition A RANKING APPROACH FOR HUMAN AGE ESTIMATION BASED ON FACE IMAGES , 2022 .

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

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

[8]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Alex M. Andrew.  An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+189 pp., ISBN 0-521-78019-5 (Hbk, £27.50) , 2000, Robotica.

[10]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[11]  Konstantinos Karantzalos,et al.  Deep Learning-Based Man-Made Object Detection from Hyperspectral Data , 2015, ISVC.

[12]  Niels da Vitoria Lobo,et al.  Age classification from facial images , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

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

[14]  Venkatesan Muthukumar,et al.  A Deep Belief Network for Classifying Remotely-Sensed Hyperspectral Data , 2015, ISVC.

[15]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

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

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[19]  Pascal Vincent,et al.  Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.

[20]  Xing Zhao,et al.  Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[22]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[23]  Nikolaos Doulamis,et al.  Deep supervised learning for hyperspectral data classification through convolutional neural networks , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[24]  R. Chellappa,et al.  Age progression in Human Faces : A Survey , 2008 .

[25]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Juha Karhunen,et al.  Principal component neural networks — Theory and applications , 1998, Pattern Analysis and Applications.

[27]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[28]  Lijuan Cao,et al.  A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine , 2003, Neurocomputing.

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

[30]  J T Todd,et al.  Perception of growth: a geometric analysis of how different styles of change are distinguished. , 1981, Journal of experimental psychology. Human perception and performance.

[31]  Hala H. Zayed,et al.  Automated Facial Age Estimation Using Deep Belief Network , 2017 .

[32]  Yu Zhang,et al.  Learning from facial aging patterns for automatic age estimation , 2006, MM '06.

[33]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

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

[35]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.