Abstract: Two-dimensional face presentation attacks are one of most notorious and pervasive face spoofing types, causing security issues to facial authentication systems. To tackle these issues, a cost-effective face anti-spoofing (FAS) system based on acoustic modality, named as Echo-FAS, is devised, which employs a crafted acoustic signal to probe the presented face. First, a large-scale, high-diversity, acoustic-based FAS database, named as Echo-Spoof, is built. Based upon Echo-Spoof, we design a two-branch framework combining global and local frequency features of the presented face to distinguish live vs. spoofing faces. Echo-FAS has the following merits: (1) it only needs one speaker and one microphone; (2) it can capture three-dimensional geometrical information of the presented face and achieve a remarkable FAS performance; and (3) it can be handily allied with RGB-based FAS models to mitigate the overfitting problem in the RGB modality and make the FAS model more accurate and robust.

Abstract: Two-dimensional face presentation attacks are one of most notorious and pervasive face spoofing types, causing security issues to facial authentication systems. To tackle these issues, a cost-effective face anti-spoofing (FAS) system based on acoustic modality, named as Echo-FAS, is devised, which employs a crafted acoustic signal to probe the presented face. First, a large-scale, high-diversity, acoustic-based FAS database, named as Echo-Spoof, is built. Based upon Echo-Spoof, we design a two-branch framework combining global and local frequency features of the presented face to distinguish live vs. spoofing faces. Echo-FAS has the following merits: (1) it only needs one speaker and one microphone; (2) it can capture three-dimensional geometrical information of the presented face and achieve a remarkable FAS performance; and (3) it can be handily allied with RGB-based FAS models to mitigate the overfitting problem in the RGB modality and make the FAS model more accurate and robust.