Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

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Images generated from a variety of sources and foundations today can pose difficulty for a user to interpret similarity in them or analyze them for further use because of their segmentation policies. This unconventionality can generate many errors, because of which the previously used traditional methodologies such as supervised learning techniques less resourceful, which requires huge quantity of labelled training data which mirrors the desired target data. This paper thus puts forward the mechanism of an alternative technique i.e. transfer learning to be used in image diagnosis so that efficiency and accuracy among images can be achieved. This type of mechanism deals with variation in the desired and actual data used for training and the outlier sensitivity, which ultimately enhances the predictions by giving better results in various areas, thus leaving the traditional methodologies behind. The following analysis further discusses about three types of transfer classifiers which can be applied using only small volume of training data sets and their contrast with the traditional method which requires huge quantities of training data having attributes with slight changes. The three different separators were compared amongst them and also together from the traditional methodology being used for a very common application used in our daily life. Also, commonly occurring problems such as the outlier sensitivity problem were taken into consideration and measures were taken to recognise and improvise them. On further research it was observed that the performance of transfer learning exceeds that of the conventional supervised learning approaches being used for small amount of characteristic training data provided reducing the stratification errors to a great extent

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