images with deep residual regressors on APPA-REAL database Eirikur - PowerPoint PPT Presentation

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Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database Eirikur Agustsson 1 , Radu Timofte 1,2 , Sergio Escalera 3,4,6 , Xavier Baro 4,5 , Isabelle Guyon 6,7 , Rasmus Rothe 2 1 Computer Vision Lab,

Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database Eirikur Agustsson 1 , Radu Timofte 1,2 , Sergio Escalera 3,4,6 , Xavier Baro 4,5 , Isabelle Guyon 6,7 , Rasmus Rothe 2 1 Computer Vision Lab, D-ITET, ETH Zurich, Switzerland 2 Merantix GmbH, Berlin, Germany FG 2017 3 Dept. Mathematics and Computer Science, UB, Spain 4 Computer Vision Center, UAB, Barcelona, Spain 5 EIMT, Open University of Catalonia, Barcelona, Spain Washington, DC. 6 ChaLearn, California, USA 7 Universite Paris-Saclay, France This work has been partially supported by the ETH General Fund (OK), European Research Council project VarCity (\#273940), a NVIDIA GPU grant, Spanish projects TIN2015-66951-C2-2-R and TIN2016-74946-P (MINECO/FEDER, UE) and CERCA Programme / Generalitat de Catalunya.
Overview APPA-REAL Database Apparent vs. Real age prediction Residual DEX regression Quantitative Results Model Visualization 2
APPA-REAL Database 7,591 Faces Real Age Labels 260,659 Apparent Age Labels Crowdsourced over the Internet http://bit.ly/APPA-REAL 3
Data Collection Framework 4
Apparent vs Real Age Apparent Labels are noisy 5
Apparent vs Real Age Apparent Labels are noisy Average Apparent age is stable Only depends on the image (with enough ratings) Can one help predicting the other? 6
DEX Age Prediction Pipeline (Rothe et al. , ICCV2015, IJCV2016) ● Pre-trained on IMDB-WIKI, 0.5M face images w/ noisy age labels ● Finetuned on APPA-REAL with same learning parameters in all experiments 7
Predicting Real Age with Apparent Real GT is not best predictor of Apparent "Wisdom of the crowd" outperforms DEX Apparent DEX marginally outperforms Real DEX with SVR adjustment How can we better utilize the Real and Apparent labels? 8
Residual DEX Train regressor D 1 for apparent age Train regressor D 2 on the residuals: where a i is the real age of face image I i Final prediction: Hope D 2 picks up facial features not captured by apparent age 9
Results for Real Age Prediction Best result obtained with Residual DEX on top of Apparent DEX Still a significant gap of ~0.7 years to the "Wisdom of the crowd" 10
Example Predictions 11
Example Predictions 12
Where are the models looking? 13
Conclusions We proposed and studied APPA-REAL - the first database with both apparent and real age labels. We showed how apparent age can help when predicting real age. We proposed Residual DEX for incorporating both apparent and real age labels. The "Wisdom of the crowd" apparent age prediction sets a new reference that has yet to be outperformed with ML models. 14

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