LEARNING DISCRIMINANT DIRECTION BINARY PALMPRINT DESCRIPTOR

Abstract:

Biometrics can provide higher identification accuracy than other authentication system, so it is more suitable for some real-world personal identification applications that need high-standard security. A biometric technology describes a set of strategies to analyse certain individuals biometric features, store and then using those patterns to identify or affirm the identity of a person. Among various biometrics methodologies, palm print identification has received more attention because of its good performance. However, due to large size of palm print images and presence of principal lines, wrinkles, creases, and other noises, there are large numbers of in accurate minutiae present. The computational need of palm print identification is likewise quite massive and it takes a variety of time to find identification of a palm print in big database. Here a novel palm print identification solution has been proposed that increases the accuracy. The palm print images are delineated and pre-processed with the help of adaptive histogram equalization. ROI extraction is used for extract the specific region in pre-processed image. Therefore, key focus of this work was to ensure best possible alignment of the local regions image blocks, as this can also address frequently observed deformations in the contactless palm print images. The algorithm to match such ROI images can be broadly divided into three steps, first identifying the grid map between two image next extracting local blocks around each of the selected grid points, and then comparing the corresponding blocks from the two images to generate the matching score. The 2 matching score of two samples are considered for in decision making. Here also implement multi model biometric system that includes face recognition and palm print verification system

Frontend:

DOTNET

Backend:

SQL SERVER

Area:

IEEE PROJECT

Domain:

IMAGE PROCESSING

Sample Screen:       

Sample Document:  

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(Full Document Contains: Content Page, Abstract, Introduction, Software Description, DFD, ER, UML Diagrams, Modules Description, System Requirements, Testing, Conclusion, Future work, Bibliography, References, Source code, All Screenshots)