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    This study introduces a novel deep reinforcement learning method for realistic face aging, improving identity preservation and aging accuracy. The approach uses two agents to better match individual aging features, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Face aging simulation is challenging due to limited longitudinal data for individuals.
    • Existing methods often map between age groups, failing to capture unique identity-specific aging features.
    • This leads to generated faces that lack realism and accurate personal aging characteristics.

    Purpose of the Study:

    • To address the limitations of current face aging techniques.
    • To develop a method that accurately captures individual identity and aging features.
    • To improve the realism and accuracy of synthetic face aging.

    Main Methods:

    • Re-annotated the CACD2000 dataset for improved training data.
    • Proposed a consensus-agent deep reinforcement learning framework.
    • Modeled face aging as a Markov decision process with two agents: aging process and aging personalization agents.
    • Agents cooperate to match aging features to individual identities.

    Main Results:

    • The proposed model demonstrates convincing performance on four face aging datasets.
    • Achieved superior results compared to current state-of-the-art face aging methods.
    • The synergistic cooperation between agents effectively enhances aging feature matching.

    Conclusions:

    • The consensus-agent deep reinforcement learning method significantly improves face aging accuracy and identity preservation.
    • This approach offers a more robust solution for generating realistic age-progressed faces.
    • The findings suggest a promising direction for future research in generative AI for facial image synthesis.