Windowed DMD as a microtexture descriptor for finger vein counter-spoofing in biometrics

Abstract

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Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decomposition

“…These modes essentially capture different large-scale to smallscale structures (sparse components) including a background structure (low-rank model) [7]. DMD has gained significant applications in various fields [2,3,16], including for detecting spoof samples from facial authentication video data sets [33] and for detecting spoofed finger-vein images [31]. The advantage of this method is its ability to identify regions of dominant motion in an image sequence in a completely data-driven manner without relying on any prior assumptions about the patterns of behaviour within the data.…”

“…2), where at first the DCE-MRI sequence is processed through window-DMD method [31] to compensate for the pseudo-periodic breathing motion (the importance of running W-DMD as a first step process is shown in Sect. 4.4).…”

“…3. Finally, we concatenate all of the c1s across all the windows to obtain W-DMD component-1 (W-DMD (C1)), and similarly, concatenation of the c2s produce W-DMD component-2 (W-DMD (C2)) [31]). 1: …”

One dimensional local binary patterns of electroencephalogram signals for detecting Alzheimer's disease

“…Contributions of the paper are as follows: Although LBP has been used extensively as a feature descriptor in the fields of image [16], [17], [22], [23], speech [18], [24] and signal processing [18], [25], its application to analysing AD patients' data is novel. We show that 1D-LBP captures the descriptive information, in terms of histograms representing the relative changes in EEG amplitudes, in a way that can be readily utilised by a support vector machine (SVM) for classification.…”

“…Although HTER has been used extensively in the field of biometrics [22], [23], its usage in biomedical applications is not very well known. HTER has the advantage of not being affected by the overwhelmingly large sample size of one class versus another because both types of errors are weighted equally, thus coercing equal contributions from both errors.…”