Abstract
Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders to gain access to the system. This paper proposes two novel features for face liveness detection systems to protect against printed photo attacks and replayed attacks for biometric authentication systems. The first feature obtains the texture difference between red and green channels of face images inspired by the observation that skin blood flow in the face has properties that enable distinction between live and spoofing face images. The second feature estimates the color distribution in the local regions of face images, instead of whole images, because image quality might be more discriminative in small areas of face images. These two features are concatenated together, along with a multi-scale local binary pattern feature, and a support vector machine classifier is trained to discriminate between live and spoofing face images. The experimental results show that the performance of the proposed method for face spoof detection is promising when compared with that of previously published methods. Furthermore, the proposed system can be implemented in real time, which is valuable for mobile applications.
Highlights
To protect personal privacy, biometric authentication systems, such as face and fingerprint recognition systems, have gained considerable attention for their ability to confirm user identity.face and fingerprint recognition systems [1,2] have been extensively researched and implemented in various security systems
Four public domain databases containing images of various 2D face spoof attacks were used to using the Viola–Jones face detection algorithm [25] and normalized the face image into a 64 × 64 pixel evaluate the performance of the proposed face liveness detection system
This study proposed a face liveness detection system that could identify printed photo attacks and replayed attacks through a single face image
Summary
Biometric authentication systems, such as face and fingerprint recognition systems, have gained considerable attention for their ability to confirm user identity. Recognition systemssystems that usethat commercial software are vulnerable to spoofing attacks usingusing face art face recognition use commercial software are vulnerable to spoofing attacks images [8] The reason for this is that live and spoofing face images of the same user may be similar in face images [8]. Such attacks on a secure eye cannot distinguish a live face image from a spoofing face image at first glance [9] Such attacks system is a substantial problem because acquiring face images or video from a camera or social media on a secure system is a substantial problem because acquiring face images or video from a camera or is easier than is acquiring other biometric traits, such as fingerprints.
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