4,810 research outputs found
Personal Identification Using Ear Recognition
Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively
The ear as a biometric
It is more than 10 years since the first tentative experiments in ear biometrics were conducted and it has now reached the “adolescence” of its development towards a mature biometric. Here we present a timely retrospective of the ensuing research since those early days. Whilst its detailed structure may not be as complex as the iris, we show that the ear has unique security advantages over other biometrics. It is most unusual, even unique, in that it supports not only visual and forensic recognition, but also acoustic recognition at the same time. This, together with its deep three-dimensional structure and its robust resistance to change with age will make it very difficult to counterfeit thus ensuring that the ear will occupy a special place in situations requiring a high degree of protection
A Robust Algorithm for Ear Recognition System Based on Self Organization Maps
This paper presents a robust algorithm for ear identification based on geometrical features of the ear and Kohnen Self Organization Maps (SOM). Using ears in identifying people has been interesting at least 100 years. The researches still discuss if the ears are unique or unique enough to be used as biometrics. Ear
shape applications are not commonly used, yet, but the area is interesting especially in crime investigation. In this paper we present the basics of using ear as biometric for person identification and authentication. High resolution ear images are taken by high resolution digital camera. Six images have been
taken for twenty three persons. Four geometrical distances were calculated for each image. These geometrical distances are used as an input to the unsupervised Kohonen self organization maps. The accuracy of identification were found to be equal to 98%, for the proposed system .We conclude that that
the proposed model gives faster and more accurate identification of persons based on the ear biometrics and it works as promising tool for person identification of persons from the mage of their ear for criminal investigation purposes
In-ear EEG biometrics for feasible and readily collectable real-world person authentication
The use of EEG as a biometrics modality has been investigated for about a
decade, however its feasibility in real-world applications is not yet
conclusively established, mainly due to the issues with collectability and
reproducibility. To this end, we propose a readily deployable EEG biometrics
system based on a `one-fits-all' viscoelastic generic in-ear EEG sensor
(collectability), which does not require skilled assistance or cumbersome
preparation. Unlike most existing studies, we consider data recorded over
multiple recording days and for multiple subjects (reproducibility) while, for
rigour, the training and test segments are not taken from the same recording
days. A robust approach is considered based on the resting state with eyes
closed paradigm, the use of both parametric (autoregressive model) and
non-parametric (spectral) features, and supported by simple and fast cosine
distance, linear discriminant analysis and support vector machine classifiers.
Both the verification and identification forensics scenarios are considered and
the achieved results are on par with the studies based on impractical on-scalp
recordings. Comprehensive analysis over a number of subjects, setups, and
analysis features demonstrates the feasibility of the proposed ear-EEG
biometrics, and its potential in resolving the critical collectability,
robustness, and reproducibility issues associated with current EEG biometrics
MobiBits: Multimodal Mobile Biometric Database
This paper presents a novel database comprising representations of five
different biometric characteristics, collected in a mobile, unconstrained or
semi-constrained setting with three different mobile devices, including
characteristics previously unavailable in existing datasets, namely hand
images, thermal hand images, and thermal face images, all acquired with a
mobile, off-the-shelf device. In addition to this collection of data we perform
an extensive set of experiments providing insight on benchmark recognition
performance that can be achieved with these data, carried out with existing
commercial and academic biometric solutions. This is the first known to us
mobile biometric database introducing samples of biometric traits such as
thermal hand images and thermal face images. We hope that this contribution
will make a valuable addition to the already existing databases and enable new
experiments and studies in the field of mobile authentication. The MobiBits
database is made publicly available to the research community at no cost for
non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted
for publication on July 20, 201
UBSegNet: Unified Biometric Region of Interest Segmentation Network
Digital human identity management, can now be seen as a social necessity, as
it is essentially required in almost every public sector such as, financial
inclusions, security, banking, social networking e.t.c. Hence, in today's
rampantly emerging world with so many adversarial entities, relying on a single
biometric trait is being too optimistic. In this paper, we have proposed a
novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for
extracting region of interest from five different biometric traits viz. face,
iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed
UBSegNet consists of two stages: (i) Trait classification and (ii) Trait
localization. For these stages, we have used a state of the art region based
convolutional neural network (RCNN), comprising of three major parts namely
convolutional layers, region proposal network (RPN) along with classification
and regression heads. The model has been evaluated over various huge publicly
available biometric databases. To the best of our knowledge this is the first
unified architecture proposed, segmenting multiple biometric traits. It has
been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and
produces very good results. Our work on unified biometric segmentation, opens
up the vast opportunities in the field of multiple biometric traits based
authentication systems.Comment: 4th Asian Conference on Pattern Recognition (ACPR 2017
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