Non-contact measurement of cardiac pulse signals has attracted high interests due to its convenience and cost effectiveness. However, extracting pulse signals on mobile handheld devices (e.g. smartphones) based on face videos captured by mobile cameras usually suffers from low measurement accuracy due to misalignment errors in face tracking and inevitable illumination changes in a mobile scenario, and low efficiency due to a handheld's limited computing power. We propose two techniques to address these limitations: 1) an accurate and efficient face tracking method based on an Active Shape Model (ASM) and the LDB (Local Difference Binary) feature description; 2) an adaptive temporal filtering method which can detect, and in turn denoise, sharp intensity changes in the source trace. Experimental results demonstrate that the proposed solution can achieve a speedup of 6.2× and is robust to noises in common mobile scenarios.
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