Face Demorphing

The morphing attack proved to be a serious threat for modern automated border control systems where face recognition is used to link the identity of a passenger to his/her e-document. In this paper, we show that by exploiting the live face image acquired at the gate, the morphed face image stored in the document can be reverted (or demorphed) enough to reveal the identity of the legitimate document owner, thus allowing the system to issue a warning. A number of practical experiments on two data sets proves the efficacy of our approach.

[1]  Tom Neubert,et al.  Face Morphing Detection: An Approach Based on Image Degradation Analysis , 2017, IWDW.

[2]  Vijay H. Mankar,et al.  Digital image forgery detection using passive techniques: A survey , 2013, Digit. Investig..

[3]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[4]  Davide Maltoni,et al.  The magic passport , 2014, IEEE International Joint Conference on Biometrics.

[5]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Kiran B. Raja,et al.  On the vulnerability of face recognition systems towards morphed face attacks , 2017, 2017 5th International Workshop on Biometrics and Forensics (IWBF).

[7]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[8]  Bernadette Dorizzi,et al.  Fingerprint and On-Line Signature Verification Competitions at ICB 2009 , 2009, ICB.

[9]  Davide Maltoni,et al.  On the Effects of Image Alterations on Face Recognition Accuracy , 2016, Face Recognition Across the Imaging Spectrum.

[10]  George Wolberg,et al.  Digital image warping , 1990 .

[11]  Robin S S Kramer,et al.  Fraudulent ID using face morphs: Experiments on human and automatic recognition , 2017, PloS one.

[12]  Sébastien Marcel,et al.  Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting , 2017, 2017 International Conference of the Biometrics Special Interest Group (BIOSIG).

[13]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Davide Maltoni,et al.  On the Feasibility of Creating Double-Identity Fingerprints , 2017, IEEE Transactions on Information Forensics and Security.

[15]  Kiran B. Raja,et al.  Transferable Deep-CNN Features for Detecting Digital and Print-Scanned Morphed Face Images , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[16]  Kiran B. Raja,et al.  Detecting morphed face images , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[17]  David F. Rogers,et al.  Mathematical elements for computer graphics , 1976 .

[18]  Anna Hilsmann,et al.  Detection of Face Morphing Attacks by Deep Learning , 2017, IWDW.