Photoplethysmography imaging can be used to extract heart rate (HR) from video. The existing deep learning and the denoising methods are not effective for video with high RGB background. This paper presents a solution to address this issue by a Bayes level set based light weight region of interest segmentation in cooperation with a convolutional attention network. Evaluated on COHFACE dataset, the proposed model shows highest HR extraction accuracy with an average absolute error of 3.058bpm, a mean square error of 0.66bpm, and a correlation coefficient of 0.848.