Biblio

Biometrics has enormous role to authenticate or substantiate an individual's on the basis of their physiological or behavioral attributes for pattern recognition system. Multimodal biometric systems cover up the limitations of single/ uni-biometric system. In this work, the multimodal biometric system is proposed; iris and hand geometry features are fused at feature level. The iris features are extracted by using moments and morphological operations are used to extract the features of hand geometry. The Chaos-based encryption is applied in order to enhance the high security on the database. Accuracy is predicted by performing the matching process. The experimental result shows that the overall performance of multimodal system has increased with accuracy, Genuine Acceptance Rate (GAR) and reduces with False Acceptance Rate (FAR) and False Rejection Rate (FRR) by using chaos with iris and hand geometry biometrics.

Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Recently, a new class of outlier detection algorithms has emerged, called contextual outlier detection, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e., lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency. We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.

Outlier detection is a fundamental data science task with applications ranging from data cleaning to network security. Recently, a new class of outlier detection algorithms has emerged, called contextual outlier detection, and has shown improved performance when studying anomalous behavior in a specific context. However, as we point out in this article, such approaches have limited applicability in situations where the context is sparse (i.e., lacking a suitable frame of reference). Moreover, approaches developed to date do not scale to large datasets. To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral model based on the relevant contexts, which is then integrated in a natural and robust fashion. We run ROCOD on both synthetic and real-world datasets and demonstrate that it outperforms other competitive baselines on the axes of efficacy and efficiency. We also drill down and perform a fine-grained analysis to shed light on the rationale for the performance gains of ROCOD and reveal its effectiveness when handling objects with sparse contexts.