From Attribute-Labels to Faces: Face Generation Using a Conditional Generative Adversarial Network

Facial attributes are instrumental in semantically characterizing faces. Automated classification of such attributes (i.e., age, gender, ethnicity) has been a well studied topic. We here seek to explore the inverse problem, namely given attribute-labels the generation of attribute-associated faces. The interest in this topic is fueled by related applications in law enforcement and entertainment. In this work, we propose two models for attribute-label based facial image and video generation incorporating 2D and 3D deep conditional generative adversarial networks (DCGAN). The attribute-labels serve as a tool to determine the specific representations of generated images and videos. While these are early results, our findings indicate the methods' ability to generate realistic faces from attribute labels.

[1]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Sepp Hochreiter,et al.  GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.

[3]  Arun Ross,et al.  What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics , 2016, IEEE Transactions on Information Forensics and Security.

[4]  Arun Ross,et al.  Impact of facial cosmetics on automatic gender and age estimation algorithms , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[5]  Antitza Dantcheva,et al.  From attributes to faces: a conditional generative network for face generation , 2018, 2018 International Conference of the Biometrics Special Interest Group (BIOSIG).

[6]  Albert Ali Salah,et al.  Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles , 2012, ECCV.

[7]  David Berthelot,et al.  BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.

[8]  Bogdan Raducanu,et al.  Invertible Conditional GANs for image editing , 2016, ArXiv.

[9]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[10]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[11]  Jean-Luc Dugelay,et al.  Bag of soft biometrics for person identification , 2010, Multimedia Tools and Applications.