3,121 research outputs found
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
A Study on Automatic Latent Fingerprint Identification System
Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research
Fingerprint Pore Detection: A Survey
This work presents the first survey on fingerprint pore detection. The survey
provides a general overview of the field and discusses methods, datasets, and
evaluation protocols. We also present a baseline method inspired on the
state-of-the-art that implements a customizable Fully Convolutional Network,
whose hyperparameters were tuned to achieve optimal pore detection rates.
Finally, we also reimplementated three other approaches proposed in the
literature for evaluation purposes. We have made the source code of (1) the
baseline method, (2) the reimplemented approaches, and (3) the training and
evaluation processes for two different datasets available to the public to
attract more researchers to the field and to facilitate future comparisons
under the same conditions. The code is available in the following repository:
https://github.com/azimIbragimov/Fingerprint-Pore-Detection-A-Surve
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