Rapid increase in population growth has made the mankind to delve in appropriate identification of individuals through biometrics. Foot Print Recognition System is a new challenging area involved in the Personal recognition that is easy to capture and distinctive. Foot Print has its own dimensions, different in many ways and can be distinguished from one another. The main objective is to provide a novel efficient automated system Segmentation using Foot Print based on structural relations among the features in order to overcome the existing manual method. This system comprises of various statistical computations of various foot print parameters for identifying the factors like Instep-Foot Index, Ball-Foot Index, Heel-Index, Toe-Index etc. The input is naked footprint and the output result to an efficient segmentation system thereby leading to time complexity.

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In modern digital era authentication has been done using biometric recognition. This biometric includes finger print, footprint, facial recognition, DNA of a person’s gene, hand palm print and eye’s iris recognition. The widely used among these is finger print and iris recognition. In this work we proposed a biometric recognition using footprints of a person. Earlier work deals with capturing footprint on a paper or on a surface. This won’t give us accurate foot print, since it depends on nature of the surface, quality of the paper and proper placement of the foot to give good foot print impression. To avoid all these we proposed a touch less method to obtain foot prints. The footprint can be obtained using any digital camera. We can take footprint image in many angles to conform the individuality of a person. In this work we used Principle Component Analysis (PCA) for pattern recognition and feature extraction. Then the SVM classifier split the patterns in to relevant classes. In early stage of our work itself we got remarkable quality and it is comparatively better than conventional footprint images obtained using paper or surface

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Security as a condition is the degree of resistance to, or protection from harm. Securing gadgets in a way that is simple for the user to deploy yet, stringent enough to deny any malware intrusions onto the protected circle is investigated to find a balance between the extremes. Basically, the dominant approach on current control access is via password or PIN, but its flaw is being clearly documented. An application (to be incorporated in a mobile phone) that allows the user's gadget to be used as a Biometric Capture device in addition to serve as a Biometric Signature acquisition device for processing a multi-level authentication procedure to allow access to any specific Web Service of exclusive confidentiality is proposed. To evaluate the lucidness of the proposed procedure, a specific set of domain specifications to work on are chosen and the accuracy of the Biometric face Recognition carried out is evaluated along with the compatibility of the Application developed with different sample inputs. The results obtained are exemplary compared to the existing other devices to suit a larger section of the society through the Internet for improving the security.

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Recently, the computed tomography (CT) and magnetic resonance imaging (MRI) medical image fusion have turned into a challenging issue in the medical field. The optimal fused image is a significant component to detect the disease easily. In this research, we propose an iterative optimization approach for CT and MRI image fusion. Initially, the CT and MRI image fusion is subjected to a multilabel optimization problem. The main aim is to minimize the data and smoothness cost during image fusion. To optimize the fusion parameters, the Modified Global Flower Pollination Algorithm is proposed. Here, six sets of fusion images with different experimental analysis are evaluated in terms of different evaluation metrics such as accuracy, specificity, sensitivity, SD, structural similarity index, feature similarity index, mutual information, fusion quality, and root mean square error (RMSE). While comparing to state‐of‐art methods, the proposed fusion model provides best RMSE with higher fusion performance. Experiments on a set of MRI and CT images of medical data set show that the proposed method outperforms a very competitive performance in terms of fusion quality.

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