Face Presentation Attack Detection by Exploring Spectral Signatures

• Published in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops pp. 672 - 679
• Main Authors: Raghavendra, R., Raja, Kiran B., Venkatesh, Sushma, Busch, Christoph
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Face; Face recognition; Printers; Sensors; Skin; Support vector machines
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW.2017.96

Presentation attack on the face recognition systems is well studied in the biometrics community resulting in various techniques for detecting the attacks. A low-cost presentation attack (e.g. print attacks) on face recognition systems has been demonstrated for systems operating in visible, multispectral (visible and near infrared spectrum) and extended multispectral (more than two spectral bands spanning from visible to near infrared space, commonly in 500nm-1000nm). In this paper, we propose a novel method to detect the presentation attacks on the extended multispectral face recognition systems. The proposed method is based on characterising the reflectance properties of the captured image through the spectral signature. The spectral signature is further classified using the linear Support Vector Machine (SVM) to obtain the decision on presented sample as an artefact or bona-fide. Since the reflectance property of the human skin and the artefact material differ, the proposed method can efficiently detect the presentation attacks on the extended multispectral system. Extensive experiments are carried out on a publicly available extended multispectral database (EMSPAD) comprised of 50 subjects with two different Presentation Attack Instrument (PAI) generated using two different printers. The comparison analysis is presented by comparing the performance of the proposed scheme with the contemporary schemes based on the image fusion and PAD score level fusion. Based on the obtained results, the proposed method has indicated the best performance in detecting both known and unknown attacks.