基于超像素分割的IPPG活体皮肤检测

The existing alive-skin detection methods exhibit low accuracy and poor real-time performance. Therefore, an image photoplethysmography (IPPG) alive-skin detection (SPASD) algorithm is proposed based on superpixel segmentation in this study. An image is segmented into multiple superpixel sub-blocks using the simple linear iterative clustering zero-parameter algorithm; subsequently, the IPPG technology is used to extract pulse signals from each sub-block in parallel. Finally, a support vector machine is used to train and classify the extracted signals for achieving real-time alive-skin detection. The experimental results demonstrate that the SPASD algorithm can effectively improve the alive-skin detection accuracy (92.02%) and real-time performance. The proposed method can be applied in face anti-fraud, non-contact physiological signal detection, facial expression recognition, and other fields.