Boosting cross-age face verification via generative age normalization

Despite the tremendous progress in face verification performance as a result of Deep Learning, the sensitivity to human age variations remains an Achilles' heel of the majority of the contemporary face verification software. A promising solution to this problem consists in synthetic aging/rejuvenation of the input face images to some predefined age categories prior to face verification. We recently proposed [3] Age-cGAN aging/rejuvenation method based on generative adversarial neural networks allowing to synthesize more plausible and realistic faces than alternative non-generative methods. However, in this work, we show that Age-cGAN cannot be directly used for improving face verification due to its slightly imperfect preservation of the original identities in aged/rejuvenated faces. We therefore propose Local Manifold Adaptation (LMA) approach which resolves the stated issue of Age-cGAN resulting in the novel Age-cGAN+LMA aging/rejuvenation method. Based on Age-cGAN+LMA, we design an age normalization algorithm which boosts the accuracy of an off-the-shelf face verification software in the cross-age evaluation scenario.

[1]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[2]  Shiguang Shan,et al.  A Compositional and Dynamic Model for Face Aging , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[4]  Ira Kemelmacher-Shlizerman,et al.  Illumination-Aware Age Progression , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[7]  Jon Gauthier.  Conditional generative adversarial nets for convolutional face generation , 2015 .

[8]  Yang Song,et al.  Age Progression/Regression by Conditional Adversarial Autoencoder , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Jean-Luc Dugelay,et al.  Face aging with conditional generative adversarial networks , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[10]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[11]  Mahadev Satyanarayanan,et al.  OpenFace: A general-purpose face recognition library with mobile applications , 2016 .

[12]  Rama Chellappa,et al.  Face Verification Across Age Progression , 2006, IEEE Trans. Image Process..

[13]  Ira Kemelmacher-Shlizerman,et al.  The MegaFace Benchmark: 1 Million Faces for Recognition at Scale , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Alexei A. Efros,et al.  Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.

[15]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[16]  Nicu Sebe,et al.  Recurrent Face Aging , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  David J. Kriegman,et al.  Localizing parts of faces using a consensus of exemplars , 2011, CVPR.

[18]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Gang Hua,et al.  Labeled Faces in the Wild: A Survey , 2016 .

[20]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[21]  Yiying Tong,et al.  Age-Invariant Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Pascal Paysan,et al.  Statistical modeling of facial aging based on 3D scans , 2010 .

[23]  龚迪洪.  Hidden Factor Analysis for Age Invariant Face Recognition , 2013 .

[24]  Yunhong Wang,et al.  Age invariant face recognition based on texture embedded discriminative graph model , 2014, IEEE International Joint Conference on Biometrics.

[25]  Shengcai Liao,et al.  Face Detection Based on Multi-Block LBP Representation , 2007, ICB.

[26]  Bernard Tiddeman,et al.  Prototyping and Transforming Facial Textures for Perception Research , 2001, IEEE Computer Graphics and Applications.

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

[28]  Anil K. Jain,et al.  Face recognition across time lapse: On learning feature subspaces , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[29]  Anil K. Jain,et al.  A Discriminative Model for Age Invariant Face Recognition , 2011, IEEE Transactions on Information Forensics and Security.

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

[31]  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).

[32]  D. Perrett,et al.  Perception of age in adult Caucasian male faces: computer graphic manipulation of shape and colour information , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[33]  Stefano Soatto,et al.  A Study of Face Recognition as People Age , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[34]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Yu Qiao,et al.  Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Chu-Song Chen,et al.  Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval , 2014, ECCV.

[37]  Haibin Ling,et al.  Cross-age face verification by coordinating with cross-face age verification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).