Advances of Science and Technology. 6th EAI International Conference, ICAST 2018, Bahir Dar, Ethiopia, October 5-7, 2018, Proceedings

Research Article

Evolving 3D Facial Expressions Using Interactive Genetic Algorithms

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  • @INPROCEEDINGS{10.1007/978-3-030-15357-1_40,
        author={Meareg Hailemariam and Ben Goertzel and Tesfa Yohannes},
        title={Evolving 3D Facial Expressions Using Interactive Genetic Algorithms},
        proceedings={Advances of Science and Technology. 6th EAI International Conference, ICAST 2018, Bahir Dar, Ethiopia, October 5-7, 2018, Proceedings},
        proceedings_a={ICAST},
        year={2019},
        month={3},
        keywords={Evolutionary algorithms Interactive genetic algorithms 3D facial expressions},
        doi={10.1007/978-3-030-15357-1_40}
    }
    
  • Meareg Hailemariam
    Ben Goertzel
    Tesfa Yohannes
    Year: 2019
    Evolving 3D Facial Expressions Using Interactive Genetic Algorithms
    ICAST
    Springer
    DOI: 10.1007/978-3-030-15357-1_40
Meareg Hailemariam,*, Ben Goertzel,*, Tesfa Yohannes,*
    *Contact email: meareg@hansonrobotics.com, ben@goertzel.org, tesfa@hansonrobotics.com

    Abstract

    Interactive Genetic Algorithms (IGA) are applied in optimization problems where the fitness function is fuzzy or subjective. Its application transcends several domains including photography, fashion, gaming and graphics. This work introduces a novel implementation of Interactive Genetic Algorithm (IGA) for evolving facial animations on a 3D face model. In this paper, an animation of a facial expression represents a chromosome; while genes are equivalent, depending on the crossover method applied, either to a keyframe point information (f-curve) of a facial bone or f-curves of grouped sub-parts such as the head, mouth or eyes. Crossover techniques uniform, cut-and-spice, blend and their hybrids were implemented with a user playing fitness function role. Moreover, in order to maximize user preference and minimize the user fatigue during evolution, sub-parts based elitism was implemented. Subjective measurements of credibility and peculiarity parameters among a given artist animated and evolved expressions were done. For the experiment results here, an average crossover percentage of 85%, a mutation level of 0.01, initial population of 36, and 8 rounds of evolution settings were considered. As detailed in the experiment section, the IGA based evolved facial expressions scored competitive results to the artist-animated ones.