Fast Dual Discrete and Complexwavelet Based Video Magnification for 3D Facial Video Identification

Magnifying micro motion of videos that are undetectable by humans has recently been popular in many applications. This is due to its impact in numerous applications. In this paper, we explore this technique in 3D facial video identification, where we try to distinguish between real 3D facial objects in videos and 2D images of faces in a video frame sequence, and utilize this in biometric identification. We present a fast 2D Dual Discrete Wavelet Transform 2D-DWT based video magnification technique that detects micro movements by magnifying the phase differences between subsequent video frame's wavelet coefficients, at different sub bands. Next, in order to overcome shortcoming of 2D-DWT systems, 2D Dual Complex Wavelet Transform 2D-CWT has also been employed to estimate phase changes between subsequent video frames at different spatial locations of Complex Wavelets sub-bands. This latter presented CWT Technique uses the Radon Transform to detect any periodic motion in the video frames. Several simulation results are given to show that our proposed hybrid technique achieves comparable and sometimes superior performance with far less complexity when compared with recent literature in micro motion magnification, such as steerable pyramids STR and Riesz Transform RT based steerable pyramids RT-STR. Both DWT and CWT techniques are combined for 3D facial video identification. The attached videos demonstrate the superior video quality obtained by the proposed technique.