Imaging photoplethysmography is an emerging technique for the extraction of biometric information from people using video recordings. The focus is on extracting the cardiac heart rate of the subject by analysing the luminance of the colour video signal and identifying periodic components. Advanced signal processing is needed to recover the information required. In this paper, independent component analysis (ICA), principal component analysis, auto- and cross-correlation are investigated and compared with respect to their effectiveness in extracting the relevant information from video recordings. Results obtained are compared with those recorded by a modern commercial finger pulse oximeter. It is found that ICA produces the most consistent results.

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Imaging photoplethysmography is a relatively new technique for extracting biometric information from video images of faces. This is useful in non-invasive monitoring of patients including neonates or the aged, with respect to sudden infant death syndrome, sleep apnoea, pulmonary disease, physical or mental stress and other cardio-vascular conditions. In this paper, we investigate the limits of detection of the heart rate (HR) while reducing the video quality. We compare the performance of three independent component analysis (ICA) methods (JADE, FastICA, RADICAL), autocorrelation with signal conditioning techniques and identify the most robust approach. We discuss sources of increasing error and other limiting conditions in three situations of reduced signal-to-noise ratio: one where the area of the analyzed face is decreased from 100 to 5%, another where the face area is progressively re-sampled down to a single RGB pixel and one where the HR signal is severely reduced with respect to the boundary noise. In most cases, the cardiac pulse rate can be reliably and accurately detected from videos containing only 5% facial area or from a face occupying just 4 pixels or containing only 5% of the facial HR modulation.

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