A Fully Automatic Framework for Prediction of 3D Facial Rejuvenation

Research output: Chapter in Book/Conference paperConference paper

Abstract

How will I look afterwards? is a common question asked by the patients undergoing a cosmetic procedure. Cosmetic practitioners at present can only offer subjective and descriptive replies. This subjective prediction is a serious concern for patients undergoing cosmetic treatment and therefore necessitates the development of automatic techniques for facial quantification. This paper proposes a novel machine learning approach to quantify and predict the outcome of 3D facial rejuvenation prior to actual cosmetic procedure. The facial rejuvenation prediction results are achieved by estimating the dermal filler volume in 3D faces. This involves estimation of structural changes in 3D
face images and to learn underlying structural mapping. Our preliminary experimental results show that the proposed model achieves superior prediction accuracy on real world dataset compared to baseline methods. The computational time analysis shows that the proposed technique is very efficient (at test time) which makes it suitable for real time applications.
Original languageEnglish
Title of host publication2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728101255
DOIs
Publication statusPublished - 4 Feb 2019
Event2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) - Auckland, New Zealand
Duration: 19 Nov 201821 Nov 2018

Conference

Conference2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
CountryNew Zealand
CityAuckland
Period19/11/1821/11/18

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