Abstract
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.
Highlights
The human visual system has far less ability to recognize spatial and temporal variations, when compared with automated computations, of frame pixel values in different videos
Magnifying micro movements in natural videos has been investigated by several researchers in the past decade. This is due to the fact that it reveals useful information to recognize little spatial and temporal variations of frame pixel values that are useful in numerous applications [1,2,3,4,5]
In order to speed up computations with Steerable pyramids; a new image pyramid representation, the Riesz Transform RT pyramid was proposed [8, 9]
Summary
The human visual system has far less ability to recognize spatial and temporal variations, when compared with automated computations, of frame pixel values in different videos. In this paper we first present a fast DWT based video magnification technique In this technique, the Approximate Reisz Transform of [8] is utilized to estimate the local phase difference between sub bands of wavelets decompositions of video frames for the purpose of fast micro movement magnification. The Approximate Reisz Transform of [8] is utilized to estimate the local phase difference between sub bands of wavelets decompositions of video frames for the purpose of fast micro movement magnification This technique has been employed for 3D facial video recognition. In order to overcome shortcomings of DWT systems, a CWT based technique is proposed In this respect, a novel approach is proposed to accurately estimate the phase differences between wavelet sub band coefficients of subsequent frames of the video.
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