Indoor positioning has garnered significant interest over the last decade due to rapidly growing demand for location-based services. As a result, multitude of techniques been proposed localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, emerged as one most popular approaches its low implementation cost. The prevailing fingerprinting-bas...

Various methods have been developed for indoor localisation using WLAN signals. Algorithms that fingerprint the Received Signal Strength Indication (RSSI) of WiFi for different locations can achieve tracking accuracies of the order of a few meters. RSSI fingerprinting suffers though from two main limitations: first, as the signal environment changes, so does the fingerprint database, which requ...

Fingerprint biometric is one of the most successful biometrics applied in both forensic law enforcement and security applications. Recent developments in fingerprint acquisition technology have resulted in touchless live scan devices that generate 3D representation of fingerprints, and thus can overcome the deformation and smearing problems caused by conventional contact-based acquisition techn...

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification system. Because minutiae matching are certainly the most well-known and widely used method for fingerprint matching, minutiae are local discontinuities in the fingerprint pattern. In this paper a fingerprint matching algorithm is proposed using some specific feature of the minu...

Fingerprint is most popular and common biometric trait used for authentication. The fingerprint is a physiological biometric characteristic to identify person. Fingerprint recognition is the problem of searching a fingerprint in the reference database to find a fingerprint that matches a given fingerprint. For enhancing the performance and accuracy of biometric fingerprint recognition system on...

We present a deep artificial neural network (DANN) model that learns latent fingerprint image patches using a stack of Restricted Boltzmann Machines (RBMs), and uses it to perform segmentation of latent fingerprint images. Artificial neural networks (ANN) are biologically-inspired architectures that produce hierarchies of maps through learned weights or filters. Latent fingerprints are fingerpr...

Minutiae-based representation is the most widely adopted fingerprint representation scheme. The compactness of minutiae template has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of the original fingerprint image. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerpri...

In this paper we study that fingerprints are the oldest and most widely used form of biometric identification. The widespread use of fingerprints, there is little statistical theory on uniqueness of fingerprint minutiae. A critical step in study statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. Automatic and reliable extraction of minutiae from the ...

Fingerprint identification and verification are one of the most significant and reliable identification methods. It is impossible that two people have the same fingerprint. Automatics identification of humans based on fingerprint requires the input fingerprint to be match with a large number of fingerprints in the database. Generally, the fingerprint recognition systems are unable to solve the ...