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Spoofing (presentation) attacks are important threats for face recognition and authentication systems, which try to deceive them by presenting an image or video of a different subject, or by using a 3D mask. Remote (non-contact) photoplethysmography (rPPG) is useful for liveness detection using a facial video by estimating the heart-rate of the subject. In this paper, we first compare the presentation attack detection performance of three different rPPG-based heart rate estimation methods on four datasets (3DMAD, Replay-Attack, Replay-Mobile, and MSU-MFSD). We also present a cascaded fusion system, which utilizes a multistage ensemble of classifiers using rPPG, motion-based (including head-pose, eye-gaze and eye-blink), and texture-based features. Experimental results show that the proposed method outperforms several other presentation attack detection methods in the literature, which utilize rPPG. [ABSTRACT FROM AUTHOR]

Background: The cardiac parameters, such as heart rate (HR) and heart rate variability (HRV), are very important physiological data for daily healthcare. Recently, the camera-based photoplethysmography techniques have been proposed for HR measurement. These techniques allow us to estimate the HR contactlessly with low-cost camera. However, the previous works showed limit success for estimating HRV because the R-R intervals, the primary data for HRV calculation, are sensitive to noise and artifacts.Methods: This paper proposed a non-contact method to extract the blood volume pulse signal using a chrominance-based method followed by a proposed CWT-based denoising technique. The R-R intervals can then be obtained by finding the peaks in the denoised signal. In this paper, we taped 12 video clips using the frontal camera of a smart phone with different scenarios to make comparisons among our method and the other alternatives using the absolute errors between the estimated HRV metrics and the ones obtained by an ECG-accurate chest band.Results: As shown in experiments, our algorithm can greatly reduce absolute errors of HRV metrics comparing with the related works using RGB color signals. The mean of absolute errors of HRV metrics from our method is only 3.53 ms for the static-subject video clips.Conclusions: The proposed camera-based method is able to produce reliable HRV metrics which are close to the ones measured by contact devices under different conditions. Thus, our method can be used for remote health monitoring in a convenient and comfortable way. [ABSTRACT FROM AUTHOR]