Back to the future: A fully automatic method for robust age progression

Sagonas, Christos, Panagakis, Yannis ORCID logoORCID:, Arunkumar, Saritha, Ratha, Nalini and Zafeiriou, Stefanos (2016) Back to the future: A fully automatic method for robust age progression. 2016 23rd International Conference on Pattern Recognition (ICPR). In: 2016 23rd International Conference on Pattern Recognition (ICPR), 04-08 Dec 2016, Cancun, Mexico. ISBN 9781509048472. [Conference or Workshop Item] (doi:10.1109/ICPR.2016.7900297)

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It has been shown that significant age difference between a probe and gallery face image can decrease the matching accuracy. If the face images can be normalized in age, there can be a huge impact on the face verification accuracy and thus many novel applications such as matching driver's license, passport and visa images with the real person's images can be effectively implemented. Face progression can address this issue by generating a face image for a specific age. Many researchers have attempted to address this problem focusing on predicting older faces from a younger face. In this paper, we propose a novel method for robust and automatic face progression in totally unconstrained conditions. Our method takes into account that faces belonging to the same age-groups share age patterns such as wrinkles while faces across different age-groups share some common patterns such as expressions and skin colors. Given training images of K different age-groups the proposed method learns to recover K low-rank age and one low-rank common components. These extracted components from the learning phase are used to progress an input face to younger as well as older ages in bidirectional fashion. Using standard datasets, we demonstrate that the proposed progression method outperforms state-of-the-art age progression methods and also improves matching accuracy in a face verification protocol that includes age progression.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23779
Notes on copyright: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
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Depositing User: Yannis Panagakis
Date Deposited: 06 Mar 2018 18:41
Last Modified: 29 Nov 2022 21:26

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