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Home / programming language / Online Matlab Projects / Deep Representation Based Feature Extraction and Recovering for Finger Vein VerificationDeep Representation Based Feature Extraction and Recovering for Finger Vein Verification
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Deep Representation Based Feature Extraction and Recovering for Finger Vein VerificationDeep Representation Based Feature Extraction and Recovering for Finger Vein Verification

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SKU: PROJ8015 Categories: 2018-2019 Projects, Digital Image Processing Projects, Online Matlab Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification

Abstract-Finger-vein biometrics has been extensively
investigated for personal verification. Despite recent advances
in finger-vein verification, current solutions completely depend
on domain knowledge and still lack the robustness to extract
finger-vein features from raw images. A deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, automatically identify two regions: A clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same
segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein pattern in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.

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