Detecting Morphed Face Images

Widespread deployment of Automatic Border Control (ABC) along with the electronic Machine Readable navel Documents (eMRTD) for person verification has enabled a prominent use case of face biometrics in border control applications. Many countries issue eMRTD passports on the basis of a printed biometric face photo submitted by the applicant. Some countries offer web-portals for passport renewal, where citizens can upload their. face photo. These applications allow the possibility of the photo being altered to beauty the appearance of the data subject or being morphed to conceal the applicant identity. Specifically, if an eMRTD passport is issued with a morphed facial image, two or more data subjects likely the known applicant and one or more unknown companion(s), can use such passport to pass a border control. In this work we propose novel scheme to detect morphed face images based on facial micro-textures extracted using statistically independent filters that are trained on natural images. Given a face image, the proposed method will obtain a micro-texture variation using Binarized Statistical Image Features (BSIF), and the decision is made using a linear Support Vector Machine (SVM). This is first work carried out towards detecting the morphed face images. Extensive experiments are carried out on a large-scale database of 450 morphed face images created using 110 unique subjects with different ethnicity, age, and gender that indicates the superior performance.