  Blood pressure (BP) monitoring is vital in daily healthcare, especially for
cardiovascular diseases. However, BP values are mainly acquired through the
contact sensing method, which is inconvenient and unfriendly to continuous BP
measurement. Hence, we propose an efficient end-to-end network to estimate the
BP values from a facial video to achieve remote BP measurement in daily life.
In this study, we first derived a Spatial-temporal map of a short-time (~15s)
facial video. According to the Spatial-temporal map, we then regressed the BP
ranges by a designed blood pressure classifier and simultaneously calculated
the specific value by a blood pressure calculator in each BP range. In
addition, we also developed an innovative oversampling training strategy to
handle the unbalanced data distribution problem. Finally, we trained the
proposed network on a private dataset ASPD and tested it on the popular dataset
MMSE-HR. As a result, the proposed network achieved a state-of-the-art MAE of
12.35 mmHg and 9.5 mmHg on systolic and diastolic BP measurements, which is
better than the recent works. It concludes that the proposed method has
excellent potential for camera-based BP monitoring in real-world scenarios.
