In recent years, the rapid development of artificial intelligence and computer vision technologies has significantly increased interest in face detection tasks. The effectiveness of face detection systems largely depends on the quality of the datasets used for training. Therefore, preparing a high-quality and diverse dataset has become a crucial scientific and practical challenge. This article provides a detailed overview of the dataset preparation process for face detection. It examines essential stages such as data collection and cleaning, face annotation, image normalization, and augmentation. These steps ensure dataset diversity and balance, which in turn contribute to the accuracy and robustness of face detection models.

The findings highlight that a well-prepared dataset not only improves the overall performance of face detection but also ensures that models remain reliable under various conditions, including changes in lighting, pose, facial expressions, and background environments.

dataset, computer vision, face detection, annotation, normalization, augmentation, artificial intelligence.

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