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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Deep Learning

    Background:

    • Synthesizing realistic profile faces aids deep pose-invariant model training for face recognition.
    • Discrepancies between synthetic and real face image distributions limit performance.
    • Costly annotation work and extreme pose data augmentation are challenges.

    Purpose of the Study:

    • To propose a Dual-Agent Generative Adversarial Network (DA-GAN) to enhance synthetic face realism.
    • To preserve identity information during realism refinement.
    • To narrow the distribution gap between synthetic and real face images.

    Main Methods:

    • Utilized a 3D face model as a simulator for generating profile faces.
    • Employed a fully convolutional network generator and an auto-encoder discriminator with dual agents.
    • Incorporated pose perception loss, identity perception loss, and adversarial loss with boundary equilibrium regularization.

    Main Results:

    • DA-GAN achieved outstanding perceptual quality in synthetic face generation.
    • Significantly outperformed state-of-the-art methods on NIST IJB-A and CFP benchmarks.
    • Secured 1st place in NIST IJB-A face recognition competition (verification and identification tracks).

    Conclusions:

    • DA-GAN effectively improves synthetic face realism while preserving identity.
    • Demonstrates superior performance in unconstrained face recognition tasks.
    • Presents a promising approach for generic transfer learning problems.