2,001 research outputs found
Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting
A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
We propose a fully automatic minutiae extractor, called MinutiaeNet, based on
deep neural networks with compact feature representation for fast comparison of
minutiae sets. Specifically, first a network, called CoarseNet, estimates the
minutiae score map and minutiae orientation based on convolutional neural
network and fingerprint domain knowledge (enhanced image, orientation field,
and segmentation map). Subsequently, another network, called FineNet, refines
the candidate minutiae locations based on score map. We demonstrate the
effectiveness of using the fingerprint domain knowledge together with the deep
networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004)
public domain fingerprint datasets provide comprehensive empirical support for
the merits of our method. Further, our method finds minutiae sets that are
better in terms of precision and recall in comparison with state-of-the-art on
these two datasets. Given the lack of annotated fingerprint datasets with
minutiae ground truth, the proposed approach to robust minutiae detection will
be useful to train network-based fingerprint matching algorithms as well as for
evaluating fingerprint individuality at scale. MinutiaeNet is implemented in
Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018
Multibiometric Authentication System Processed by the Use of Fusion Algorithm
The present day authentication system is mostly uni-model i.e having only single authentication method which can be either finger print, iris , palm veins ,etc. Thus these models have to contend with a variety of problems such as absurd or unusual data, non-versatility; un authorized attempts, and huge amount of error rates. Some of these limitations can be reduced or stopped by the use of multimodal biometric systems that integrate the evidence presented by several sources of information. This paper converses a multi biometric based authentication system based on Fusion algorithm using a key. Our work mainly focuses on the fusion algorithm, i.e fusion of finger and palm print out of which the greatest features from the above two traits are taken into account. With minimum possible features the fusion of the both the traits is carried out. Then some key is generated for multi biometric authentication. By processing the test image of a person, the identity of the person is displayed with his/her own image. By the fusion algorithm, it is found that it has less computation time compared to the existing algorithms. By matching results, we validate and authenticate the particular individual
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