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Machine Vision and Applications

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17.09.2018 | Original Paper

Age progression by gender-specific 3D aging model

verfasst von: Sidra Riaz, Unsang Park, Jongmoo Choi, Prem Natarajan

Erschienen in: Machine Vision and Applications | Ausgabe 1/2019

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Abstract

Facial aging is an important problem of face recognition in missing children and automatic template update. As aging is a temporal process, it alters the facial appearance of the individuals. The sources of variations in facial appearance are caused by wrinkles (under eyes, forehead, around lips, and jawline), facial growth (cranial size and skull), and skin tone. The other factors such as health, lifestyle, and gender also impose variations in the aging process. Therefore, predicting facial aging with considering all those factors is a very difficult task. We present our 3D gender-specific aging model which automatically produces simulated images at age y by taking only one input image at age x irrespective of the pose and lighting conditions. The gender-specific aging model is constructed by various datasets (FG-NET, PCSO, Celebrities, BROWNS, Private), and its quality is evaluated with respect to various combinations of the datasets. We further fine-tune the aging model by changing the length of shape and texture eigenvectors and examine how these parameters affect the simulation results. Comparisons of the simulation results with state-of-the-art approaches as well as ground truth images demonstrate the effectiveness of the proposed methods. The subjective and objective evaluations are also carried out which emphasize the potential of our proposed gender-specific 3D aging model.

Vorheriger Artikel A probabilistic analysis of a common RANSAC heuristic

Nächster Artikel Robust multi-lane detection and tracking using adaptive threshold and lane classification

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Titel: Age progression by gender-specific 3D aging model
verfasst von: Sidra Riaz
Unsang Park
Jongmoo Choi
Prem Natarajan
Publikationsdatum: 17.09.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Machine Vision and Applications / Ausgabe 1/2019
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-018-0975-2

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