In order to provide accurate recognition of individuals, the most discriminating information present in an iris pattern must be extracted. Only the significant features of the iris must be encoded so that comparisons between templates can be made. Most iris recognition systems make use of a band pass decomposition of the iris image to create a biometric template. In this paper, the feature extraction techniques are improved and implemented. These techniques are using wavelet filters. The encoded data by wavelet filters are converted to binary code to represent the biometric template. The Hamming distance is used to classify the iris templates, and the False Accept Rate (FAR), False Reject Rate (FRR) and recognition rate (RR) are calculated [1].The wavelet transform using DAUB12 filter proves that it is a good feature extraction technique. It gives equal FAR and FRR and a high recognition rate for the two used databases. When applying the DAUB12 filter to CASIA database, the FAR and FRR are equal to 1.053%, while the recognition rate is 97.89%. For Bath database the recognition rate when applying DAUB12 filter is 100%. CASIA and Bath databases are obtained through personal communication. These databases are used in this paper.

Iris recognition is regarded as the most reliable and accurate biometric identification system available. The work presented in this paper involves improving iris segmentation to reduce execution time. To determine the performance of the iris system two databases of digitized grayscale eye images are used.The segmentation process in the iris recognition system is used to localize the circular iris and pupil regions, excluding eyelids and eyelashes. New techniques are proposed and implemented for pupil detection. These techniques are mask, profile and the combined profile mask (CPM) technique. The extracted iris region is normalized into a rectangular block with constant dimensions to account for imaging inconsistencies.The feature extraction technique is based on 2D Gabor filters. The Hamming distance is used to classify the iris templates, and the FAR, FRR and RR are calculated.The results of the study proved that the best technique for pupil detection is when using the combined technique. It gives about 100% success rate for pupil detection.

In this work, A proposed Algorithm has been constructed for the selecting the best band and lessening high dimension of remote sensing data depending on multi algorithms, each on carried out and its results are studied irrespective of other, then combining them in the proposed algorithms, in the principle component analysis algorithm find covariance matrix for the processing bands . Then find Eigen vector by using Jacobs's method and this represents the highest value in Eigen vector. The algorithm was applied on many groups of multispectral image for the Mapper sensor. By applying it on the first group of images it concluded that the sixth band is the best one, because the value of its Eigen vector is the biggest one. when the algorithm was applied on the second group of images it concluded that the second band is the best one, and the value of its Eigen vector is the biggest one, when the algorithm was applied on the third group of images it concluded that the fifth band is the best, and the value of its Eigen vector is the biggest one (regarding separating the sixth infrared band in the three groups By using wavelet transform algorithm for one level of analysis and selecting the best band according to the least value of mean square error , to show the result of selecting the best ,the k_means algorithm was used to classify images By using K_mean classification algorithm in images .A new way was proposed to determine centers which is an important matter in accurate classifications and specifying initial centers by finding the maximum value and minimum value and finding the mean between them until getting the wanted number of centers. The algorithm was applied on three groups of multispectral images .the classification was done on total number of bands to product one band out of it.A new algorithm was constructed depending on the previous three algorithms which applies the wavelet transform on multispectral images and finding the signal to noise ratio depending on variance of each band And arranging it decendingly and then choosing the bands that have highest datas in order to select the best bands and apply the principle component analysis on it. After finding the Eigen vector from the algorithm and selecting the highest values from it, it will be classified.From applying the proposed algorithm it has been clear that it is the best in accordance to applying .because it has shown high efficiency and accuracy in classification and in finding the best band.

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Ant Colony Optimization (ACO) is a method of heuristic search using in general artificial intelligence (swarm intelligence) to simulate the behavior of the aggregate food for ants to find new solutions to the combinatorial optimization problems. Artificial ant's behavior depends on the trails of real ant with additional capabilities to make it more effective such as a memory to save the past events. Every ant build solutions to the problem, and uses the information grouped about the features and performance of the private problem, to change the look to the ant problem. In this work, an edge detection technique based on Ant Colony Optimization is used by selecting pheromone matrix which represents the information about edges in each pixel based on the guidelines set up by the ant on the image. Multiple values for different sizes of neighbor pixels are applied and a heuristic information function to test results is proposed. The results show high accuracy in edge detection of different biomedical images with different neighbors, the proposed algorithm is implemented in C Sharp 2008 language which provides high-efficiency software visible language and speed. A comparative study is also given illustrating the superiority of the proposed algorithm.