Recent studies have found that blood volume pulse (BVP) in facial videos contains features highly correlated to blood pressure (BP). However, the mapping from BVP features to BP varies from person to person. To address this issue, VidBP has been proposed as a BP detector that can be calibrated based on an individual's data. VidBP is pre-trained on a large dataset to extract BP-related features from BVP. Then, BVP samples and BP labels of an individual are fed into the pre-trained VidBP to create a personal dictionary of BP-related features. When estimating the individual's BP, the current BP-related feature is compared to the features saved in the dictionary, and the BP labels of the similar features are considered as the BP estimate. The performance of VidBP was evaluated on 640 samples of 16 subjects, and it demonstrated significantly lower errors in BP estimation compared to state-of-the-art methods. The personalized calibration of VidBP is a significant advantage, enabling it to better capture the unique mapping from BVP features to BP for each individual.Clinical relevance This study reports a feasible method to estimate BP from facial videos, providing a convenient and cost-effective way for home BP monitoring.