Image life trails based on contrast reduction models for face counter-spoofing

Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cu...

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Vydáno v:EURASIP Journal on Information Security Ročník 2023; číslo 1; s. 1 - 31
Hlavní autoři: Katika, Balaji Rao, Karthik, Kannan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 16.01.2023
Springer Nature B.V
SpringerOpen
Témata:
Calibration
Communications Engineering
Contrast reduction
Customization
Datasets
Engineering
Errors
Face counter-spoofing
Image contrast
Image enhancement
Image life trail
Image sequencing
Iterated function
Iterative methods
Logistic maps
Networks
Security Science and Technology
Self-shadows
Shadows
Signal,Image and Speech Processing
Spoofing
Systems and Data Security
Self-shadows
Face counter-spoofing
Image life trail
Contrast reduction
Logistic maps
Iterated function
ISSN:2510-523X, 1687-4161, 2510-523X, 1687-417X
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Shrnutí:Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Differences taken across this image sequence help in bringing out the self-shadows already present in the original image. The proposed solution has two fronts: (i) a calibration and customization heavy 2-class client specific model construction process, based on self-shadow statistics, in which the model has to be trained with respect to samples from the new environment, and (ii) a subject independent and virtually environment independent model building procedure using random scans and Fourier descriptors, which can be cross-ported and applied to new environments without prior training. For the first case, where calibration and customization is required, overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.3106%, and the error rates for other datasets such OULU-NPU and CASIA-SURF were 1.1928% and 2.2462% respectively. For the second case, which involved building a 1-class and 2-class model using CASIA alone and testing completely on OULU, the error rates were 5.86% and 2.34% respectively, comparable to the customized solution for OULU-NPU.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2510-523X
1687-4161
2510-523X
1687-417X
DOI:10.1186/s13635-022-00135-8