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.

The translation divergence is a challenging problem in the area of machine translation. Language divergence arises when lexically and syntactically similar sentences of the source language do not translate into sentences that are similar in lexical and syntactic structure in the target language. A detailed study of divergence issues in machine translation is required for their proper classification and detection. Though a number of Machine Translation developers have given attention to this problem, it is difficult to derive general strategies which can be used for any language pair. In this paper, we discuss the translation pattern between English to Marathi and Marathi to English of various constructions to identify the structural and syntactic divergences in English-Marathi language pair.

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