Simultaneous magnification of subtle motions and color variations in videos using riesz pyramids

Videos often contain subtle motions and color variations that cannot be easily observed. Examples include, for instance, head motion and changes in skin face color due to blood flow controlled by the heart pumping rhythm. A few techniques have been developed to magnify these subtle signals. However, they are not easily applied to many applications. First of all, previous techniques were targeted specifically towards magnification of either motion or color variations. Trying to magnify both aspects applying two of these tech niques in sequence does not produce good results. We present a method for magnifying subtle motions and color variations in videos simultaneously. Our approach is based on the Riesz pyramid, which was previously used only for motion magnification. Besides modifying the local phases of the coefficients of this pyramid, we show how altering its local amplitudes and its residue can be used to produce intensity (color) magnification. We demonstrate the effectiveness of our technique in multiple videos by revealing both subtle signals simultaneously. Finally, we also developed an Android application as a proof-of-concept that can be used for magnifying either motion or color changes

Eliminating Physiological Information from Facial Videos

© 2017 IEEE. Vital signs, cognitive load, and stress can be remotely measured from human faces using video-capturing devices under ambient light, which raises both wide applications and privacy issues. To avoid immoral use of this technology, there is a need for methods to eliminate physiological information from facial videos without affecting their visual appearance. To meet the need, we develop a novel algorithm based on motion component magnification that inputs a video and outputs its replica with physiological signals removed. Facial video data has been collected from 18 participants in a study to assess the performance of our algorithm in thwarting heart rate measurement based on remote photoplethysmography. Our results show that the mean absolute error of heart rate measurement averaged among participants was increased from 0.254 beats per minute to above 17 beats per minute without causing visible artifact. This is the first demonstration of an algorithm that can achieve this kind of functionality