Ecole d'ingénieur et centre de recherche en Sciences du numérique

Boosting cross-age face verification via generative age normalization

Antipov, Grigory; Baccouche, Moez; Dugelay, Jean-Luc

IJCB 2017, International Joint Conference on Biometrics, October 1-4, 2017, Denver, Colorado, USA

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.

Document Hal Bibtex

Titre:Boosting cross-age face verification via generative age normalization
Département:Sécurité numérique
Eurecom ref:5333
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Bibtex: @inproceedings{EURECOM+5333, year = {2017}, title = {{B}oosting cross-age face verification via generative age normalization}, author = {{A}ntipov, {G}rigory and {B}accouche, {M}oez and {D}ugelay, {J}ean-{L}uc}, booktitle = {{IJCB} 2017, {I}nternational {J}oint {C}onference on {B}iometrics, {O}ctober 1-4, 2017, {D}enver, {C}olorado, {USA}}, address = {{D}enver, {\'{E}}{TATS}-{UNIS}}, month = {10}, url = {} }
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