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LrPPG

Detailed implementation settings of CFrPPG

Figure 1.

The 3 local regions for template training are defined based on the landmark index in Figure 1 as follows: ROI.template(1).ptsIdx = [0 1 2 3 4 48 49 50 51 33 30 29 28 27 39 40 41 36];

ROI.template(2).ptsIdx = [27 28 29 30 33 51 62 63 54 12 13 14 15 16 45 46 47 42];

ROI.template(3).ptsIdx = [3 4 5 6 7 8 9 10 11 12 13 75 74 33 73 72];

The 9 local regions for feature construction are defined based on the landmark index in Figure 1 as follows: ROI.feature(1).ptsIdx = [0 1 2 3 72 73 33 30 29 28 27 39 40 41 36];

ROI.feature(2).ptsIdx = [36 68 72 73 33 74 75 71 45 46 47 42 27 39 40 41];

ROI.feature(3).ptsIdx = [27 28 29 30 33 74 75 13 14 15 16 45 46 47 42];

ROI.feature(4).ptsIdx = [1 2 3 4 48 61 62 33 30 69 68];

ROI.feature(5).ptsIdx = [68 72 48 61 62 63 54 75 71 70 30 69];

ROI.feature(6).ptsIdx = [30 33 51 62 63 54 12 13 14 15 71 70];

ROI.feature(7).ptsIdx = [3 4 5 6 7 8 66 62 33 73 72];

ROI.feature(8).ptsIdx = [72 48 50 6 7 8 9 10 55 54 75 74 33 73];

ROI.feature(9).ptsIdx = [33 62 57 8 9 10 11 12 13 75 74];

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cfrppg's Issues

关于源码

您好,我读过这篇关于CFrPPG的论文"Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection".想把这个思路进行实验,但是我的水平有限,只是一枚小白,所以想请问一下有相关源码吗可以提供吗?我是一名研二的学生,只是用于我的学校工作中。
希望您看到可以回复一下。
万分感激~~

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