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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| Published in: | EURASIP Journal on Information Security Vol. 2023; no. 1; pp. 1 - 31 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Springer International Publishing
16.01.2023
Springer Nature B.V SpringerOpen |
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| ISSN: | 2510-523X, 1687-4161, 2510-523X, 1687-417X |
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| Abstract | 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. |
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| AbstractList | Abstract 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. 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. |
| ArticleNumber | 1 |
| Author | Karthik, Kannan Katika, Balaji Rao |
| Author_xml | – sequence: 1 givenname: Balaji Rao orcidid: 0000-0002-7315-142X surname: Katika fullname: Katika, Balaji Rao email: k.balaji@iitg.ac.in organization: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati – sequence: 2 givenname: Kannan surname: Karthik fullname: Karthik, Kannan organization: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati |
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| Keywords | Self-shadows Face counter-spoofing Image life trail Contrast reduction Logistic maps Iterated function |
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| Snippet | 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... Abstract Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the... |
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| SubjectTerms | 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 |
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| Title | Image life trails based on contrast reduction models for face counter-spoofing |
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| Volume | 2023 |
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