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The premise is that a biometric is a measurable physical characteristic which are reliable than passwords. Iris biometry is used to recognize an individual in a natural and intuitive way. Secure communications and mobile commerce are some of the application areas. Iris based security applications thrive on infrared cameras and video cameras for logins and transaction authentications. Accuracy, algorithm speed and template size are attributes that are important for large-scale identity programs and national database applications. In this paper, different iris recognition methods which aid an appropriate outlook for future work to build integrated classifier on latest input devices for excellent business transactions are discussed. Benchmark databases, products are also discussed. Since the area is currently one of the most on the go and the bulk of research is very large, this survey covers some of the significant methods.

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Clustering divides the data available as bulk into meaningful, useful groups (Clusters) without any prior knowledge about the data. Cluster analysis provides an abstraction from individual data objects to the clusters in which those objects reside. It is a key technique in the data mining and has become an important issue in many fields. This paper presents a novel Fractional Lion Algorithm (FLA) as an optimization methodology for the clustering problems. The proposed algorithm utilizes the lion's unique characteristics such as pride, laggardness exploitation, territorial defence and territorial take over. The Lion algorithm is modified with the fractional theory to search the cluster centroids. The proposed fractional lion algorithm estimates the centroids with the systematic initialization itself. Proposed methodology is a robust one, since the parameters utilized are insensitive and not problem dependent. The performance of the proposed rapid centroid estimation is evaluated using the cluster accuracy, jaccard coefficient and rand coefficient. The quality of this approach is evaluated on the benchmarked iris and wine data sets. On comparing with the particle swarm clustering algorithm, experimental results shows that the clustering accuracy of about 75% is achieved by the proposed algorithm.

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Problem statement: Gaze estimation systems compute the direction of eye gaze based on observed eye movements. The need for gaze-contingent applications is the basis of the current research work. The gaze pointing systems is a substitute for the existing input devices. Approach: The gaze tracking methods are either feature based or appearance based. In this study, an appearance based approach for gaze tracking is proposed based on Run Length Coding (RLC). The experiment was conducted considering transitional changes and the class-intervals in iris pixels. The image acquisition begins from the center of the screen in anticlockwise direction. The center of the screen was the pivot point. Results: Using RLC, the recognition rate of 95% was achieved. The image analysis in different directions determines the gaze point. The directions was determined with respect to the pivot point. Conclusion:The proposed system provides a robust, less computational gaze tracking method using web camera.