Deep Modeling of Human Age Guesses for Apparent Age Estimation

In this paper we propose a unique deep learning formulation of the apparent age estimation problem, using the APPA-Real dataset. APPA-Real is a dataset containing 7, 591 face images, where each image is labeled by a set of approximately 38 guesses of the facial age. All guesses are collected from human labelers. In our approach, we first generate per-image label distributions from the human guesses, and then learn label distributions with convolutional neural networks and the KL-divergence loss function. We provide comparisons to models trained with other objective functions. We achieve state-of-the-art results for apparent age estimation on the APPA-Real dataset with a mean absolute error of 3.688, outperforming other methods using the same dataset.

[1]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[2]  Xin Geng,et al.  Logistic Boosting Regression for Label Distribution Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Hazim Kemal Ekenel,et al.  How Transferable Are CNN-Based Features for Age and Gender Classification? , 2016, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG).

[4]  Kai Zhao,et al.  Label Distribution Learning Forests , 2017, NIPS.

[5]  Stefan Winkler,et al.  Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning , 2015, ICMI.

[6]  Jianxin Wu,et al.  Deep Label Distribution Learning With Label Ambiguity , 2016, IEEE Transactions on Image Processing.

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

[8]  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.

[9]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[10]  Anil K. Jain,et al.  Age estimation from face images: Human vs. machine performance , 2013, 2013 International Conference on Biometrics (ICB).

[11]  Andreas Lanitis,et al.  A survey of the effects of aging on biometric identity verification , 2010, Int. J. Biom..

[12]  Zhi-Hua Zhou,et al.  Facial Age Estimation by Learning from Label Distributions , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Xu Yang,et al.  Sparsity Conditional Energy Label Distribution Learning for Age Estimation , 2016, IJCAI.

[15]  Xin Geng,et al.  Label Distribution Learning , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[16]  Xin Geng,et al.  Pre-release Prediction of Crowd Opinion on Movies by Label Distribution Learning , 2015, IJCAI.

[17]  Xavier Baró,et al.  Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[18]  Sergio Escalera,et al.  ChaLearn looking at people: A review of events and resources , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

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

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