Face recognition has become a widely adopted method for user authentication and identification, with applications in various domains such as secure access, law enforcement, and locating missing persons. The success of this technology is largely attributed to deep learning, which leverages large datasets and effective loss functions to achieve highly discriminative features. Despite its advancements, face recognition still faces challenges in areas such as explainability, demographic bias, privacy and robustness against aging, pose variations, illumination changes, occlusions, and expressions. Additionally, the emergence of privacy regulations has led to the discontinuation of several well-established datasets, raising legal, ethical, and privacy concerns. To address these issues, synthetic facial data generation has been proposed as a solution. This technique not only mitigates privacy concerns but also allows for comprehensive experimentation with facial attributes that cause bias, helps alleviate demographic bias, and provides complementary data to enhance models trained with real data. Competitions, such as the FRCSyn and SDFR, have been organized to explore the limitations and potential of face recognition technology trained with synthetic data. This paper compares the effectiveness of established synthetic face datasets with different generation techniques in face recognition tasks. We benchmark the accuracy of seven mainstream datasets, providing a vivid comparison of approaches that are not explicitly contrasted in the literature. Our experiments highlight the diverse techniques used to address the synthetic facial data generation problem and present a comprehensive benchmark of the area. The results demonstrate the effectiveness of various methods in generating synthetic facial data with realistic variations, evidencing the diverse techniques used to deal with the problem.