Biometric System is used for person's recognition and identification for various applications. The Biometric system is unimodal and multimodal biometric system. Unimodal Biometric suffers from Noisy data, Intra class variation, non versality, spoofing etc. These drawbacks can remove by using Multimodal Biometric system. We developed the multimodal Biometric system by using Face and fingerprint Multimodalities. This system takes the advantage of individual Biometric System. This paper presents the fusion of face and fingerprint modalities at score level fusion. The system extracts the features and these features are then used for matching. Euclidean distance matcher is used for Face and Finger print modalities. Fingerprint recognition can be done with the help of minutiae matching and Gabor filter. The Face feature is extracted with the help of PCA (Principle Component Analysis) for dimensionality Reduction.Then the match scores are Normalized and sum score level fusion is used to develop the system. The proposed approach provides the better results. The Recognition Rate is increased and the error rate is decreased by with the help of this system.
Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.
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