A method for contactless blood pressure determination of a human subject, the method executed on one or more processors, the method comprising: receiving a captured image sequence of light re-emitted from the skin of one or more humans; determining , using a trained hemoglobin concentration, HC, changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject, the HC changes machine learning model being trained with a HC changes training set comprising captured image sequences and ground truth bit values from bitplanes in image sequences representative of HC changes of the subject as input; determining a blood flow data signal of one or more predetermined regions of interest, ROIs, of the subject captured on the images based on the bit values from the set of bitplanes that represent the HC changes; applying a plurality of band-pass filters, each having a separate passband, to each of the blood flow data signals to produce a bandpass filter, BPF, signal set for each ROI; extracting one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs, the domain knowledge signals comprising attributes, qualities, or features determinable from the blood flow data signal; building a trained blood pressure machine learning model, the blood pressure machine learning model being trained with a blood pressure training set comprising the BPF signal set for each of the ROIs, the one or more domain knowledge signals and ground truth blood pressure data as input; determining , using a blood flow data signal at each ROI as an input to the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure for the human subject; and outputting the determination of blood pressure. receiving a captured image sequence of light re-emitted from the skin of one or more humans; determining , using a trained hemoglobin concentration, HC, changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject, the HC changes machine learning model being trained with a HC changes training set comprising captured image sequences and ground truth bit values from bitplanes in image sequences representative of HC changes of the subject as input; determining a blood flow data signal of one or more predetermined regions of interest, ROIs, of the subject captured on the images based on the bit values from the set of bitplanes that represent the HC changes; applying a plurality of band-pass filters, each having a separate passband, to each of the blood flow data signals to produce a bandpass filter, BPF, signal set for each ROI; extracting one or more domain knowledge signals associated with the determination of blood pressure from the blood flow data signal of each of the ROIs, the domain knowledge signals comprising attributes, qualities, or features determinable from the blood flow data signal; building a trained blood pressure machine learning model, the blood pressure machine learning model being trained with a blood pressure training set comprising the BPF signal set for each of the ROIs, the one or more domain knowledge signals and ground truth blood pressure data as input; determining , using a blood flow data signal at each ROI as an input to the blood pressure machine learning model trained with the blood pressure training set, an estimation of blood pressure for the human subject; and outputting the determination of blood pressure. <EOS>