Abstract
Face recognition is a representative biometric that can be easily used; however, spoofing attacks threaten the security of face biometric systems by generating fake faces. Thus, it is not advisable to only consider sophisticated spoofing cases, such as three-dimensional masks, because they require additional equipment, thereby increasing the implementation cost. To prevent easy face spoofing attacks through print and display, the two-dimensional (2D) image analysis method using existing face recognition systems is reasonable. Therefore, we proposed a new database called the “pattern recognition-face spoofing advancement database” that can be used to prevent such attacks based on 2D image analysis. To the best of our knowledge, this is the first face spoofing database that considers the changes in both the angle and distance. Therefore, it can be used to train various positional relationships between a face and camera. We conducted various experiments to verify the efficiency of this database. The spoofing detection accuracy of our database using ResNet-18 was found to be 96.75%. The experimental results for various scenarios demonstrated that the spoof detection performances were better for images with pinch angle, near distance images, and replay attacks than those for front images, far distance images, and print attacks, respectively. In the cross-database verification result, the performance when tested with other databases (DBs) after training with our DB was better than the opposite. The results of cross-device verification in terms of camera type showed negligible difference; thus, it was concluded that the type of image sensor does not affect the detection accuracy. Consequently, it was confirmed that the proposed DB that considers various distances, capture angles, lighting conditions, and backgrounds can be used as a training DB to detect spoofing attacks in general face recognition systems.
Highlights
Nowadays, biometrics provide reliable indicators for individual recognition and authentication problems [1]
Various features that are used to detect fake face data can be extracted from each instance of data via local binary pattern (LBP), convolutional neural network (CNN), discrete cosine transform (DCT), and Laplacianfaces [7,8,9,10]
When the spoofing attacks were attempted at close distances, the focus often did not match with the device for face recognition
Summary
Biometrics provide reliable indicators for individual recognition and authentication problems [1]. As the biometric identifiers are inherent to individuals, it is difficult to manipulate, share, or overlook these traits [2]. These systems have been used in various fields such as cell phone encryption and internet banking authentication. The technique used by a face recognition system, which includes face detection and recognition, is one of the most convenient and useful practices [3,4,5,6]. The face recognition system uses a non-invasive method, and the face images have more complex biometric features compared to others. Various features that are used to detect fake face data can be extracted from each instance of data via local binary pattern (LBP), convolutional neural network (CNN), discrete cosine transform (DCT), and Laplacianfaces [7,8,9,10]
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