Günay Yılmaz, Asuman; Turhal, Uğur; Nabiyev, Vasif

Biometric recognition systems are frequently used in daily life although they are vulnerable to attacks. Today, especially the increasing use of face authentication systems has made these systems the target of face presentation attacks (FPA). This has increased the need for sensitive systems detecting the FPAs. Recently surgical masks, frequently used due to the pandemic, directly affect the performance of face recognition systems. Researchers design face recognition systems only from the eye region. This motivated us to evaluate the FPA detection performance of the eye region. Based on this, in cases where the whole face is not visible, the FPA detection performance of other parts of the face has also been examined. Therefore, in this study, FPA detection performances of facial regions of wide face, cropped face, eyes, nose, and mouth was investigated. For this purpose, the facial regions were determined and normalized, and texture features were extracted using powerful texture descriptor local binary patterns (LBP) due to its easy computability and low processing complexity. Multi-block LBP features are used to obtain more detailed texture information. Generally uniform LBP patterns are used for feature extraction in the literature. In this study, the FPA detection performances of both uniform LBP patterns and all LBP patterns were investigated. The size of feature vector is reduced by principal component analysis, and real/fake classification is performed with support vector machines. Experimental results on NUAA, CASIA, REPLAY-ATTACK and OULU-NPU datasets show that the use of all patterns increased the performance of FPA detection.

AtıfScopus18·WoS11·OpenAlex19

İncele

Engin, M. Alptekin; Cavusoglu, Bulent

Demand for better retrieval methods continue to outstrip the capabilities of available technologies despite the rapid growth of new feature extraction techniques. Extracting discriminatory features that contain texture specific information are of crucial importance in image indexing. This paper presents a novel rotation invariant texture representation model based on the multi-resolution curvelet transform via co-occurrence and Gaussian mixture features for image retrieval and classification. To extract these features, curvelet transform is applied and the coefficients are obtained at each scale and orientation. The Gaussian mixture model (GMM) features are computed from each of the sub bands and co-occurrence features are computed for only specific sub band. Rotation invariance is provided by applying cycle-shift around the GMM features. The proposed method is evaluated on well-known databases such as Brodatz, Outex_TC_00010, Outex_TC_00012, Outex_TC_00012horizon, Outex_TC_00012tl84, Vistex and KTH-TIPS. When the feature vector is analyzed in terms of its size, it is observed that its dimension is smaller than that of the existing rotation-invariant variants and it has a very good performance. Simulation results show a good performance achieved by combining different techniques with the curvelet transform. Proposed method results in high degree of success rate in classification and in precision-recall value for retrieval.

AtıfScopus8·WoS5·OpenAlex6

İncele