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Ensuring the security of healthcare data is becoming an increasingly important problem as modern technology is integrated into existing medical services. As a consequence of the adoption of healthcare data in the health care sector, it is becoming more and more common for a health professional to edit and view a patient's record using a tablet PC. To protect the patient's privacy, a secure authentication system to access patient records must be used. Yet, most Health apps used by consumers do not fall under federal or regional health privacy laws, even when the apps are used to manage a chronic illness. To solve this issue multi-biometric authentication is performed in this work via the use of deep learning classifier. This paper analyzes the performance of combining the use of on-line signature and fingerprint authentication to perform robust user authentication. Signatures are verified using the dynamic time warping (DTW) technique of string matching. The proposed minutiae-based matching algorithm, stores merely a small number of minutiae points, which greatly reduces the storage requirement with the help of phase correlation. Here, matching score level fusion is used by applying weighted sum rule for the biometric fusion process. To improve the authentication performance, deep learning classifier is proposed in this work for multi-biometrics authentication. When a biometric authentication request is submitted, the proposed authentication system uses deep learning to automatically select an appropriate matching image. In the experiment, biometric authentication was performed on healthcare in the UCI database. Multi -Biometric Authentication was used during the authentication stage.

Multimodal biometrics technology that uses more than two sorts of biometrics data has been universally applied for person certification and proof. Researchers have advised that the ear may have benefits over the face for biometric recognition. In this study, a technique for face and ear recognition has suggested. The face image and ear images are prearranged as input. From the pre-processed input images, the shape and texture characteristics are removed. The shape of ear and face is attained by suggesting modified region growing algorithm and texture characteristic by Local Gabor XOR Pattern (LGXP) method. To produce the fuzzy vault, the multi-modal biometric template and the input key are employed. For working out, the multi-modal biometric template from face and ear will be erected and it is united with the stored fuzzy vault to produce the final key. Experimental results of suggested method explain promising development in multimodal biometric validation.

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