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Facial ageing and rejuvenation modeling including lifestyle behaviours, using biometrics-based approaches

Abstract : The main focus of this thesis is to model the evolution trajectory of human face from infancy to senility using the biometrics facial features.The manifestation of facial changes caused by ageing depends on different factors such as genetic, ethnicity and lifestyle. Nevertheless, individuals in the same age group share some facial similarities. These resemblances can be employed to approximate the facial appearance of an individual in the bygone or the forthcoming years.Unlike numerous studies dealing with predictive face ageing models, for the first time, this thesis proposes the first Backward Facial Ageing Model aiming at digitally rejuvenate an adult face appearance down to its early childhood. We also present the Forward Facial Ageing Model to predict the adult face appearance in its future by taking into account the naturalageing trajectory. The main purpose of Forward Facial Ageing Model is to have a base model for the supplementary ageing models such as behavioural models.In this thesis for the first time in face ageing studies, the effects of different lifestyle behaviours are integrated into the facial ageing models. The Behavioural Facial Ageing Models predict the feature of a young face in case of having the high-risk lifestyle habits. The main attempt of these models is to illustrate the adverse effects of unsafe lifestyle behaviourson the senility of the face, aiming to prevent the youth from becoming involved in these habits. The Facial Ageing Modeling Database, contains over 1600 facial images, is collected to construct the models and 30 Face Templates for the purpose of the face ageing studies.Besides, the Face Time-Machine Database from 120 subjects is created and published to testand evaluate the results. For the proposed approach face contour and different components are modified non-linearly based on an estimated geometrical model related to the trajectory of growth or ageing. Moreover, the face texture is adapted by mapping a Face Template to the estimated geometrical model. Then, the effects of each lifestyle habit are set up to the primal predictive model.The evaluations of the results indicate that the proposed models are remarkably accurate to estimate the correct face appearance of an individual in the target age. While the simulated facial images are realistic and have the appearance, geometrical and textural characteristics of the target age, the personal identity and details of the input face images are preserved
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Submitted on : Friday, April 6, 2018 - 11:57:12 AM
Last modification on : Friday, October 4, 2019 - 1:10:30 AM


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  • HAL Id : tel-01760426, version 1



Elham Farazdaghi. Facial ageing and rejuvenation modeling including lifestyle behaviours, using biometrics-based approaches. Signal and Image Processing. Université Paris-Est, 2017. English. ⟨NNT : 2017PESC1236⟩. ⟨tel-01760426⟩



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