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Generating Face Images With Attributes for Free.

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    Synthesizing face images with arbitrary attributes overcomes data limitations for fine-grained facial recognition. This novel approach generates realistic images, significantly improving attribute recognition model performance.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Facial recognition models struggle with fine-grained attributes (emotion, age, gender) due to large, long-tail label spaces.
    • Collecting sufficient, balanced data for these attributes is expensive, leading to inferior recognition models.
    • Imbalanced training samples hinder the development of robust attribute recognition systems.

    Purpose of the Study:

    • To develop a method for synthesizing face images with arbitrary attribute combinations to address data scarcity.
    • To bridge the semantic gap between high-level attribute labels and low-level facial image synthesis.
    • To improve the performance of fine-grained facial attribute recognition models.

    Main Methods:

    • A novel neural network approach maps target attribute labels to an embedding vector.
    • A pretrained image decoder synthesizes face images from the embedding vector.
    • Perceptual loss is employed to ensure synthesized images are faithful to target attributes.

    Main Results:

    • The proposed method successfully generates photorealistic face images from attribute labels.
    • Synthesized face images, used as augmented training data, significantly boost attribute recognition model performance.
    • Experimental results validate the effectiveness of the attribute synthesis and its positive impact on recognition tasks.

    Conclusions:

    • Automated synthesis of face images with diverse attributes is feasible and effective.
    • This approach offers a cost-efficient solution to data imbalance in fine-grained facial attribute recognition.
    • The developed technique has the potential to advance the field of facial analysis and recognition.