This paper employs the Center Symmetric Local Binary Pattern (CSLBP) algorithm for extracting fine features in facial expression recognition. Additionally, the Rotation Invariant Local Phase Quantization (RILPQ) algor...
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Automatically recognising people by their biometric characteristics is a well-established research area. Biometric systems are vulnerable to many different types of presentation attacks made by persons showing photo, ...
详细信息
Automatically recognising people by their biometric characteristics is a well-established research area. Biometric systems are vulnerable to many different types of presentation attacks made by persons showing photo, video, or mask to spoof the real identity. This study introduces a novel approach to detect face-spoofing, by extracting the local features local binary pattern (LBP) and simplified weber local descriptor (SWLD) encoded convolutional neural network (CNN) models, WLD and LBP features are combined together to ensure the preservation of the local intensity information and the orientations of the edges. These two components are complementary to each other. Specifically, differential excitation preserves the local intensity information but omits the orientations of edges. On the contrary, LBP describes the orientations of the edges but ignore the intensity information, the proposed approach presents a very low degree of complexity which makes it suitable for real-time applications, Finally, a non-linear supportvectormachine (SVM) classifier with kernel function was used for determining whether the input image corresponds to a live face or not. Authors' experimental analysis on two publicly available databases REPLAY-ATTACK and CASIA face anti-spoofing showed that their approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.
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