Face recognition technology has rapidly progressed due to a growing need for security across various sectors, necessitating comprehensive datasets for effective model training.
For face recognition systems to be reliable, they require exposure to diverse data, as lacking this can lead to biased or unreliable performance in detection.
While synthetic datasets can enhance data availability for training face recognition models, they have not yet reached a point where they can entirely substitute for real-world datasets.
The performance of face recognition models is heavily influenced by the size of the datasets used for training, emphasizing the critical need for extensive data acquisition.
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