scholarly journals Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

2021 ◽  
Vol 3 (2) ◽  
pp. 131-143
Author(s):  
Vijayakumar T.
Keyword(s):  
Feature Fusion ◽  
Developed Countries ◽  
Biometric Recognition ◽  
Accurate Identification ◽  
Multiple Features ◽  
Research Attention ◽  
Research Article ◽  
Biometric Systems ◽  
Palm Print ◽  
Multimodal Biometric Recognition

Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.

Feature fusion of palmprint and face via tensor analysis and curvelet transform

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
X. Xu ◽  
X. Guan ◽  
D. Zhang ◽  
X. Zhang ◽  
W. Deng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Curvelet Transform ◽  
Image Features ◽  
Tensor Analysis ◽  
Small Samples ◽  
Palmprint Recognition ◽  
Biometric Recognition ◽  
Fusion Level ◽  
Knn Classifier ◽  
Multimodal Biometric Recognition

AbstractIn order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique

2013 ◽  
Vol 5 (3) ◽  
pp. 22-32
Author(s):  
Dhiman Karmakar ◽  
Madhura Datta ◽  
C.A. Murthy
Keyword(s):  
Selection Method ◽  
Estimation Technique ◽  
Minimal Spanning Tree ◽  
Biometric Recognition ◽  
Multimodal Biometrics ◽  
Iris Biometrics ◽  
Set Estimation ◽  
Biometric Systems ◽  
Multimodal Biometric Recognition ◽  
Better Than

Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique.

scholarly journals Estimation of Determinants of Farmland Abandonment and Its Data Problems

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 596
Author(s):  
Shinichi Kitano
Keyword(s):  
Census Data ◽  
Evaluation Research ◽  
Developed Countries ◽  
Third Party ◽  
Objective Data ◽  
Farmland Abandonment ◽  
Subjective Data ◽  
Research Attention ◽  
Abandoned Farmland ◽  
The Impact

Abandoned farmland is particularly problematic in developed countries where agriculture has a comparative disadvantage in terms of effective use of land resources invested over time. While many studies have estimated the causes of these problems, few have discussed in detail the impact of data characteristics and accuracy on the estimation results. In this study, issues related to the underlying data and the estimation of the determinants of farmland abandonment were examined. Most previous studies on farmland abandonment in Japan have used census data as the basis of their analyses. However, census data are recorded subjectively by farmers. To address this, surveys of abandoned farmland are being conducted by a third party, and the results are compiled into a geographic information system (GIS) database. Two types of datasets (subjective census data and objective GIS data) were examined for their estimation performance. Although the two sets of data are correlated, there are considerable differences between them. Subjective variables are compatible with subjective data, and objective variables are compatible with objective data (meaning that parameters are easily identified). Original data for analysis, such as policy variables, are compatible with objective data. In policy evaluation research, attention should be paid to objective data collection.

A Multimodal Biometric Recognition of Touched Fingerprint and Finger-Vein

2011 ◽  
Author(s):  
Young Ho Park ◽  
Dat Nguyen Tien ◽  
Hyeon Chang Lee ◽  
Kang Ryoung Park ◽  
Eui Chul Lee ◽  
...  
Keyword(s):  
Biometric Recognition ◽  
Finger Vein ◽  
Multimodal Biometric Recognition

Easterlin Cycles: Fertility and the Labor Market

1998 ◽  
Author(s):  
C. Y. Cyrus Chu
Keyword(s):  
Labor Market ◽  
Crude Birth Rate ◽  
Developed Countries ◽  
Theoretical Framework ◽  
Quality Data ◽  
Population Fluctuations ◽  
Research Attention ◽  
Adult Age ◽  
Group B ◽  
Second Period

I mentioned in chapter 7 that the fluctuation of human population can be summarized into three broad categories: the pretransitional, transitional, and posttransitional cycles. Among these three categories, the last one has caught the attention of most demographic economists in the past thirty years. The main reason for this unbalanced research attention is that the posttransitional cycles appear only in developed countries, where high-quality data are available for empirical research. The recent development of advanced mathematical tools also facilitates the analysis of posttransitional density-dependent population dynamics. In this chapter we will provide a summary of the theoretical and empirical analyses of the most typical population fluctuations in the posttransitional period: the so-called Easterlin cycles. The well-known Easterlin cycles, named after the pioneer work by Richard Easterlin (1961, 1980), describe the observed two-generation-long birth cycles in the twentieth-century United States and in several other developed countries. Easterlin believed that there were two features associated with the observed cycles: they are related to the labor market, and they are more or less “self-generating” (Easterlin, 1961). The first feature implies that a complete theoretical framework should characterize how people’s fertility behavior is affected by the labor market and how the labor market is affected by the fertility pattern. The second feature addresses whether the theoretical framework can generate a persistent fertility fluctuation. An ideal theoretical framework should embody both of these features, and an ideal empirical analysis should also be compatible with these features. We start the background introduction by studying a Malthusian model presented by Lee (1974). Let us consider an overlapping-generation framework in which each individual lives one or two periods. The first period is childhood, the second period is adulthood, and all surviving adults will be in the labor force. Lee wrote down the following two equations: . . . W(t) = f(L(t)), (10.1). . . . . . b(t) = g(W(t)), (10.2). . . where W(t) is the wage rate (at time t), L is the size of the adult age group, b is the crude birth rate, and f(.) and g(.) are functions with f'(.) < 0 and g'(.) > 0.

Overview

2011 ◽  
pp. 1-23
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Pattern Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Biometric System ◽  
Biometric Recognition ◽  
Biometric Data ◽  
Pattern Recognition System ◽  
Biometric Systems ◽  
History Of

A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric data discrimination and multi-biometrics, to enhance the recognition performance of biometric systems. In Section 1.1, we discuss the necessity, importance, and applications of biometric recognition technology. A brief introduction of main biometric recognition technologies are presented in Section 1.2. In Section 1.3, we describe two advanced biometric recognition technologies, biometric data discrimination and multi-biometric technologies. Section 1.4 outlines the history of related work and highlights the content of each chapter of this book.

Sign in / Sign up

Export Citation Format

Share Document