Ear biometric verification approach based on morphological and geometric invariants

<p>Biometric verification based on ear features is modern filed for scientific research. As known, there are many biometric identifiers that can identify people such as fingerprints, iris and speech. In this paper, the focus is placed on the ear biometric model in order to verifying the identity of persons. The main idea is based on used the moments as ear feature extractors. The proposed approach included some operations as follow: image capturing, edge detection, erosion, feature extraction, and matching. The proposed approach has been tested using many images of the ears with different states. Experimental results using several trails verified that the proposed approach is achieved high accuracy level over a wide variety of ear images. Also, the verification process will be completed by matching query ear image with ear images that kept in database during real time.</p>

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.

Algorithm for generating matrices of estimated effects for breeding evaluation of small cattle using a mixed biometric model

The use of a mixed biometric model for breeding evaluation of small cattle has been discussed in the article. This model of breeding evaluation involves a large number of matrix operations. At the same time, the volumes of the formed matrices are directly proportional to the number of animals in the evaluated sample as well as to the number of their off spring. An algorithm for generating matrices of estimated effects that have a large dimension has been presented in the paper. This task is the most time-consuming when using a mixed biometric model. Currently, there are the large number of mathematical packages that provide ample opportunities for performing calculations. A special place in this series is occupied by the integrated mathematical package MATLAB has been designed specifically for performing matrix operations. The authors rely on the use of this package in their work. At the same time the algorithm presented in this paper has the property of universality and can be applied by users in any other software product. Since the matrices of the estimated effects consist of zeros and ones we propose the two-step procedure for forming these matrices. At the first stage, a zero matrix of the required dimension is created. At the second stage, in accordance with the data on the number of evaluated animals, the number of herds for which off spring are distributed, the number and affiliation of evaluated animals to genetic groups, the elements of the matrix are determined, in which zeros are replaced by ones. The advantage of the proposed algorithm is its versatility, and the representation of the algorithm in the form of a block diagram will allow you to design it as a separate proceduresubroutine.

An Improved Multi-Biometric System for Authentication

Biometrics is the new technology for calculating and measuring the body parts of a person. It is playing an important role in identifying an individual. It signifies a metrics related to person characteristics (physiological or behavioral). Biometric system may be based on single modal or multiple modals. Multimodal system also termed as multi-biometric system (hybrids two or more modals) are becoming popular. The idea behind the paper is to implement and improve the authentication processusing multiple trait for identifying a person. Here, the combination of Iris & Fingerprint based biometric model is presented. The relevant features (key-points) are extracted from these two traits in parallel and then they are passed through matching module. The key-points are extracted using Discrete Wavelet Transform (DWT) and Speeded-Up Robust Feature (SURF) descriptor and then passed to the matching (mapping) module where mapping is done using the Normalized Weighted Sum-Rule. The experimental result showed that the proposed multi-biometric model performs well showing clear variation in FAR and FRR against the existing models.

Feature Extraction of Iris and Palmprint Biometric System using Dwt

Much work is done on Iris Recognition, since few years. Many cases discussed about performance in view of image capturing and recognition. Daugman work is the most important related to iris biometric in early research. It is fair to say, it is base model for iris biometric. Almost the available iris systems are based on this work. A palm print is image of the palm area of a hand. It is either an image taken online or offline. It is one of the most familiar and promising biometric model for personal identity verification. It is tough task to differentiate lines and wrinkles without explicit definition. depends on the thickness and position of some key points we can define principal lines. In our work, we are taken the principal line magnitude is less than or equal to 1. we cannot consider broken lines. If it is the case broken point is treat as last point

Mathematical methods in the breeding evaluation of small horned ruminants

Aim. Traditionally, prediction of breeding values of male small horned ruminants (rams) by referring to levels of economically useful traits of their progeny is carried out by methods of statistical analysis. However, at the same time, there is a forecasting method based on the use of a mixed biometric model. The solution of the system of equations constituting a mixed biometric model is associated with certain difficulties caused by the peculiarity of the system matrix. It is proposed to use integrated mathematical packages in the forecast, by which the system of equations can be solved in several ways, followed by analysis of the results. The prediction of progeny values is carried out by statistical methods using three statistical tests, as well as with the use of a mixed biometric model. It is of interest to compare estimates obtained by using statistical methods with estimates using a mixed biometric model. Material and Methods. The initial data set was the live weight of Qigai rams, the progeny of a group of sixteen rams belonging to eight genetic groups. Results. It was found that the forecast of breeding values of each animal using a mixed biometric model substantially clarifies the rank of each animal in the group being evaluated. Conclusion. The refinement of the estimation of breeding value is related to the effects of the genetic groups to which the animals belong in the mixed model, as well as the degree of relationship between them. Also the mixed model also allows one to isolate environmental effects from the overall assessment. Solving the system of equations in several ways will improve the reliability of the forecast.

Heritability Coverage of Standard Biometric Models under various Phenotypic Architectures

The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is now widely acknowledged that the standard biometric model can be expected to misrepresent, in manifold ways, the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. Herein, we aimed to shed some light on this general issue, by undertaking three simulation studies. In each, the parameter recovery performance of five modes- Falconer's coefficient and the SEM models, ACDE, ADE, ACE, and AE- was investigated when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was { ΣMZ,T, ΣDZ,T, ΣMZ,A, ΣDZ,A}; in study 2, the architecture was identical to that of study 1, but the informational input was { ΣMZ,T, ΣDZ,T}; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was {ΣMZ,T, ΣDZ,T, ΣMZ,A, ΣDZ,A}. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimation employed. In general, the results showed that more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/

Identity Management Based on Frontal Facial Recognition for Voters Register in Zambia

Biometric technology offers a great opportunity to identify individuals, authenticate individuals and separate individuals. Using these advantages, an election or voting model can be developed to perform elections for a country such as Zambia. Zambia currently uses a manual based voting or election model that heavily relies on paper presented documents that must be physically verified and or matched to existing prior collected information before an individual is allowed to participate in an election or a voting system. This paper proposes a frontal facial election based biometric model that can be used to rid the current election system of redundancy and introduce a paperless, accurate and efficient identification, authentication and voting process. A baseline study conducted shows that biometric authentication based on this proposed model improves a work related process such as a voting system. We start by introducing the elements that make a biometric model ideal, we then give an insight into the Zambian based election system and then we review various biometric technologies available and then finally introduce our biometric model.