Impact of Digital Face Beautification in Biometrics

Nelida Mirabet-Herranz (1) , Chiara Galdi (1) , Jean-Luc Dugelay (1)

1 Eurecom [Sophia Antipolis]

Face retouching is a widespread procedure available across a huge spectrum of modern applications. Among them, social media offer different filters to beautify face pictures by performing operations such as skin smoothing, addition of virtual makeup, as well as deforming certain facial features, for instance by widening the eyes or making the nose thinner. In this work, the effect of different facial feature modification filters (FFMF) on face recognition (FR), gender classifiers and a weight estimator are studied. To this end, popular FFMF are applied to face images of the publicly available CALFW and VIP attribute databases. Such filters distort or modify biometric features, affecting the ability of automatic FR systems to recognize individuals. The results show that the application of FFMF to face images penalizes the accuracy of FR systems and affects the estimation of other facial traits such as gender and weight.

Mots clés: Digital beautification; Social media filters; Face recognition; Gender classification; Weight estimation

Domaines: Ingénierie assistée par ordinateur

Nelida Mirabet-Herranz, Chiara Galdi, Jean-Luc Dugelay. Impact of Digital Face Beautification in Biometrics. EUVIP 2022, 10th European Workshop on Visual Information Processing, Sep 2022, Lisbon, Portugal. ⟨hal-03790638v2⟩