Open AccessBackward face ageing model (B‐FAM) for digital face image rejuvenation
Author(s) -
Farazdaghi Elham,
NaitAli Amine
Publication year - 2017
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2016.0079
Subject(s) - computer science , face (sociological concept) , artificial intelligence , facial recognition system , computer vision , field (mathematics) , process (computing) , texture mapping , texture (cosmology) , pattern recognition (psychology) , machine learning , image (mathematics) , mathematics , social science , sociology , pure mathematics , operating system
Facial ageing modelling has been an active research topic in the field of anthropology. Considering the fact that ageing is a non‐uniform and a non‐linear process for different face types (e.g. origins, gender etc.), dealing with a reliable face‐ageing model may considerably help investigators working in some specific fields such as forensics. Unlike numerous studies dealing with forward or predictive face models, in this study, the authors propose a backward model aiming at estimating childhood face images using their corresponding adult face appearance as an input. For the proposed approach, face contour and different components are modified non‐linearly, based on an estimated geometrical model. On the other hand, the face texture is estimated by mapping a reference face texture to the estimated geometrical model. This approach will show that it will be possible to ‘digitally’ rejuvenate an adult person's face down to it being 3–4 years old. For evaluation purposes, a database has been created from 112 subjects. Results have been evaluated using both objective (face recognition system) and subjective (human perception) criteria. The most promising and interesting results will be highlighted further ahead.