<ul><li>Large-scale datasets are essential for training effective face recognition (FR) systems, but acquiring real-world data presents numerous challenges like ethical and privacy concerns.</li><li>Synthetic data is a potential solution to overcome these challenges, but it is not yet a substitute for real-world datasets.</li><li>Synthetic datasets can be more cost-effective, easier to obtain, and do not raise issues like consent for use, privacy compliance, and bias. They also allow for the creation of controlled environments for testing and tuning FR models.</li><li>However, synthetic datasets lack the diversity and complexity of real-world datasets and fail to capture all the variations present in real data, resulting in worse model performance.</li><li>Synthetic data holds promise for advancing FR technology, but it is essential to recognize its current limitations. The quality of synthetic face data is catching up to real-world data, with data generation techniques improving, but it may still be a while before synthetic data eliminates the need for real-world face data for FR training.</li></ul>

Synthetic Data in Face Recognition: A Game Changer or Just Hype?

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