SummaryThe development of electronic exchanges has created a more important interest in fast and accurate customer IDs and verification. Structures, bank records, and PC framework often use personally identifiable access code IDs and test numbers (PINs) for reliable status. Biometric verification requires a level of security, unchanged structure, spoofing, and reliability. In this research work, a biometric authentication system was developed to provide security for access to the ATM network. Honest technology used for security purposes to provide efficient ATM network security is a finger vein. This method undergoes image processing technology such as preprocessing, feature extraction, and classification to locate the finger vein region for accessing the ATM network. Finally, the cascade neural network acts as a classifier method to differentiate registered and unregistered user. The performance analysis of this method is simulated by MATLAB and the process is evaluated to measure the efficient security of the network. The dataset is segregated into two stages; one for training and other for testing. The features extracted with the help of hidden layers with neurons. The matched finger vein is compared to check the accuracy.
The scale of picture collecting and data storage technologies is growing, as is the enlargement of quantity and quality of the image DB. Previously, retrieval of photos was accomplished through verbal descriptions and manual labelling of photographs, labour-intensive process. The necessity is for efficient systems termed content-based image retrieval systems to manage big collections. In this situation, the image’s visual contents, such as the shape, layout, and colour of the items included in the picture, are taken into account, as well as the image’s related data. These systems are more efficient and faster than other traditional methods of picture retrieval. In this paper, a novel method for extracting features using Gabor filtering is presented, which is then optimised through lion optimization. Finally, SVM is employed with cuckoo search optimization, while the decision tree technique is used with lion search optimization. The proposed method is put to the test on a variety of parameters, and the results reveal that Lion optimization outperforms cuckoo search.
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