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This paper discusses about Enhanced iris recognition which is used to overcome some of the problem like to automate the recognition of the iris by reducing complexity and increasing algorithm speed. Various challenges are faced while working with the iris recognition system. Iris recognition systems make use of the uniqueness of the iris patterns to derive a unique mapping. Iris recognition, as a biometric method, outperforms others because of its high accuracy. Iris recognition also has the ability to handle very large populations at high speed. Mostly three stages are followed while working with iris system i.e. preprocessing, feature extraction and recognition stage. This paper presents an automated and novel iris recognition system where overall computational match speed is reduced (from iris preprocessing to the final stage of recognition) and hence makes system more reliable with accuracy of 99.38% and low FAR.

Bacterial Foraging Optimization (BFO) is optimization technique proposed by K. M. Passino in 2002 To tackle complex search problems of the real world, scientists have been drawing inspiration from nature and natural creatures for years. Bacterial Foraging Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. A Color images Quantization is necessary if the display on which a specific image is presented works with less colors than the original image. While a lot of color reduction techniques exist in the literature, they are mainly designed for image compression as they tend to alter image color structure and distribution, the researchers are always finding alternative strategies for color quantization so that they may be prepared to select the most appropriate technique for the color quantization. The objective of this research work, is to implement a new algorithm for Color Image Quantization based on Bacteria Foraging Optimization. To compare the designed algorithm with other swarm intelligence techniques and to validate the proposed work. The proposed algorithm is then applied to commonly used images including the phantom images. The conducted experiments indicate that proposed algorithm generally results in a significant improvement of image quality compared to other well-known approaches.