A comparative study of human facial age estimation: handcrafted features vs. deep features

In recent times, the topic of human facial age estimation attracted much attention. This is due to its ability to improve biometrics systems. Recently, several applications that are based on the demographic attributes estimation have been developed. These include law enforcement, re-identification in videos, planed marketing, intelligent advertising, social media, and human-computer interaction. The main contributions of the paper are as follows. Firstly, it extends some handcrafted models that are based on the Pyramid Multi Level (PML) face representation. Secondly, it evaluates the performance of two different kinds of features that are handcrafted and deep features. It compares handcrafted and deep features in terms of accuracy and computational complexity. The proposed scheme of study includes the following three main steps: 1) face preprocessing; 2) feature extraction (two different kinds of features are studied: handcrafted and deep features); 3) feeding the obtained features to a linear regressor. In addition, we investigate the strengths and weaknesses of handcrafted and deep features when used in facial age estimation. Experiments are run on three public databases (FG-NET, PAL and FACES). These experiments show that both handcrafted and deep features are effective for facial age estimation.

[1]  Jiwen Lu,et al.  Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation , 2015, IEEE Transactions on Image Processing.

[2]  Fadi Dornaika,et al.  Age estimation in facial images through transfer learning , 2018, Machine Vision and Applications.

[3]  Vasif V. Nabiyev,et al.  Facial age estimation using spatial Weber Local Descriptor , 2016, 2016 39th International Conference on Telecommunications and Signal Processing (TSP).

[4]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[6]  Chu-Song Chen,et al.  Joint Estimation of Age and Expression by Combining Scattering and Convolutional Networks , 2018, ACM Trans. Multim. Comput. Commun. Appl..

[7]  Theo Gevers,et al.  Expression-Invariant Age Estimation Using Structured Learning , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Yao Zou,et al.  Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network , 2020, Symmetry.

[9]  Denise C. Park,et al.  A lifespan database of adult facial stimuli , 2004, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[10]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Matti Pietikäinen,et al.  Age Classification in Unconstrained Conditions Using LBP Variants , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

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

[14]  Xiaolong Wang,et al.  A study on human age estimation under facial expression changes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Timothy F. Cootes,et al.  Overview of research on facial ageing using the FG-NET ageing database , 2016, IET Biom..

[16]  Albert Ali Salah,et al.  Kernel ELM and CNN Based Facial Age Estimation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[17]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[20]  Esa Rahtu,et al.  BSIF: Binarized statistical image features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[21]  Manisha Sawant,et al.  Age estimation using local direction and moment pattern (LDMP) features , 2019, Multimedia Tools and Applications.

[22]  Yassine Ruichek,et al.  Two-stages based facial demographic attributes combination for age estimation , 2019, J. Vis. Commun. Image Represent..

[23]  Congyan Lang,et al.  General structured sparse learning for human facial age estimation , 2017, Multimedia Systems.

[24]  Vasif V. Nabiyev,et al.  Age Estimation Based on Hybrid Features of Facial Images , 2015, ISCIS.

[25]  Jiwen Lu,et al.  Ordinal Deep Learning for Facial Age Estimation , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[26]  Thomas S. Huang,et al.  Human age estimation using bio-inspired features , 2009, CVPR.

[27]  Petra Grd,et al.  Introduction to Human Age Estimation Using Face Images , 2013 .

[28]  Natalie C. Ebner,et al.  FACES—A database of facial expressions in young, middle-aged, and older women and men: Development and validation , 2010, Behavior research methods.

[29]  Yan Wang,et al.  Deep Differentiable Random Forests for Age Estimation , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Meng Wang,et al.  Facial Age Estimation With Age Difference , 2017, IEEE Transactions on Image Processing.

[31]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Hanjiang Lai,et al.  Personalized Age Progression with Bi-Level Aging Dictionary Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Fadi Dornaika,et al.  Personality Traits and Job Candidate Screening via Analyzing Facial Videos , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[34]  Katarzyna Stapor,et al.  Deep Learning Features for Face Age Estimation: Better Than Human? , 2018, BDAS.

[35]  Fadi Dornaika,et al.  Pyramid multi-level features for facial demographic estimation , 2017, Expert Syst. Appl..

[36]  Haibin Liao.  Facial age feature extraction based on deep sparse representation , 2018, Multimedia Tools and Applications.

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

[38]  D. Berry,et al.  Perceiving character in faces: the impact of age-related craniofacial changes on social perception. , 1986, Psychological bulletin.

[39]  Yun Fu,et al.  Age Synthesis and Estimation via Faces: A Survey , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Jinhui Tang,et al.  Age progression: Current technologies and applications , 2016, Neurocomputing.

[41]  Kishor Bhurchandi,et al.  Hierarchical Facial Age Estimation Using Gaussian Process Regression , 2019, IEEE Access.

[42]  Fadi Dornaika,et al.  Robust regression with deep CNNs for facial age estimation: An empirical study , 2020, Expert Syst. Appl..

[43]  Hanjiang Lai,et al.  Personalized Age Progression with Aging Dictionary , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[44]  Jiwen Lu,et al.  Group-aware deep feature learning for facial age estimation , 2017, Pattern Recognit..

[45]  Jiwen Lu,et al.  Label-Sensitive Deep Metric Learning for Facial Age Estimation , 2018, IEEE Transactions on Information Forensics and Security.

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

[47]  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).

[48]  A. Gunay,et al.  Automatic age classification with LBP , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[49]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[50]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..

[51]  Jules-Raymond Tapamo,et al.  Age estimation via face images: a survey , 2018, EURASIP Journal on Image and Video Processing.

[52]  Abdelmalik Taleb-Ahmed,et al.  Facial age estimation using BSIF and LBP , 2016, ArXiv.

[53]  Omkar M. Parkhi,et al.  VGGFace2: A Dataset for Recognising Faces across Pose and Age , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[54]  Bo Wang,et al.  Deep Regression Forests for Age Estimation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[56]  Caifeng Shan.  Learning local features for age estimation on real-life faces , 2010, MPVA '10.

[57]  Hanjiang Lai,et al.  Kinship-Guided Age Progression , 2016, Pattern Recognit..

[58]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[59]  Shuicheng Yan,et al.  What Shall I Look Like after N Years? , 2015, ACM Multimedia.