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Age progression by gender-specific 3D aging model

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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.

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Acknowledgements

This research is based upon work supported in part by Institute for Information & communications Technology Promotion (IITP) Grant funded by the Korea government (MSIT) (2017-0-01772. Development of QA system for video story understanding to pass Video Turing Test) and Information & communications Technology Promotion(IITP) grant funded by the Korea government (MSIT) (2017-0-01781. Data Collection and Automatic Tuning System Development for the Video Understanding), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2013R1A1A1061400), and the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA contract Number 2014-14071600011. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, Korea

    Sidra Riaz & Unsang Park

  2. Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA, USA

    Jongmoo Choi

  3. Information Science Institute, University of Southern California, Los Angeles, CA, USA

    Prem Natarajan

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  1. Sidra Riaz
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  2. Unsang Park
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  3. Jongmoo Choi
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  4. Prem Natarajan
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Correspondence to Unsang Park.

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Riaz, S., Park, U., Choi, J. et al. Age progression by gender-specific 3D aging model. Machine Vision and Applications 30, 91–109 (2019). https://doi.org/10.1007/s00138-018-0975-2

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