Abstract
AbstractRecently, remote heart rate (HR) estimation has attracted increasing attention. Some previous methods employ the Spatial-Temporal Map (STMap) of facial sequences to estimate HR. However, STMap ignores that each facial regions play a different role in HR estimation task. Moreover, how to focus on key facial regions to improve the performance of HR estimation is also a challenging problem.To overcome this issue, this paper proposes a novel Spatial-Channel Mixed Attention Module (SCAM) to select the facial regions with high correlation for HR estimation adaptively. To our best knowledge, we are the first to design an attention module for STMap in the HR estimation task. Furthermore, the whole HR estimation framework SCANet is proposed, including feature extraction, signal generation, and HR regression.The experiments performed on three benchmark datasets show that the proposed method achieves better performance over state-of-the-art methods.KeywordsRemote heart rate estimationSpatial-temporal mapSpatial-channel mixed attention
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