scholarly journals A Multimodal Biometric System using Iris and Palmprint

2020 ◽  
Vol 9 (3) ◽  
pp. 1892-1896
Keyword(s):  
Binary Code ◽  
Wavelet Packet ◽  
Current Approach ◽  
Weighted Sum ◽  
Fusion Method ◽  
Biometric System ◽  
Error Level ◽  
Multimodal Biometric System ◽  
Palm Print ◽  
Different Levels

A biometric system is basically a system of image recognition that uses bio metric characteristics to identify individuals. The thesis introduces a biometric multimodal system that is based on iris-based Palm Print verification and fusion. We suggest an approach to extracting features from each modality using four-level decomposition of the wavelet packet. It includes 256 packets capable of generating a simple binary code. Dictate standardized thresholds based on the first three highest energy peaks that would impact 0 or 1 for each wavelet packet. Specific fusion approaches were evaluated at different levels: character level, score level and error level. Its first fusion is an iris and palm print application, actually. For matching ratings the next one uses a weighted sum law. The next applies to the Hamacher t-norm's deficiencies. The standard database is used for testing the program proposed. The current approach and then each fusion method was checked for The consistency about the database of Casia iris merged with the database of Casia palm print. With each fusion process, the proposed solution to the multimodal biometric system achieves an increase in identification.

Score Level Fusion of Multispectral Palmprint with Triangular Conorm

2018 ◽  
Vol 6 (6) ◽  
pp. 88
Author(s):  
Maria Afzal ◽  
Mohd Abdul Ahad ◽  
Jyotsana Grover
Keyword(s):  
Experimental Results ◽  
Biometric System ◽  
Score Level Fusion ◽  
Physical Body ◽  
Multimodal Biometric System ◽  
Single Body ◽  
Multimodal Systems ◽  
Palm Print ◽  
Level Fusion ◽  
State Of Art

Biometricplay vigorous role in the authentication of user by using his/her physical body traits. Unimodal biometric system uses single body traits and multimodal systems use multiple body traits. Multimodal biometric system have overcome the disadvantages that has occurred in unimodal systems. In this paper we are fusing the different spectral bandsof palm print (Red, Green and Blue) using T-conorm operators like Hamacher, Frank, Probabilistic and Scheiwer & Sklar. Experiment Results suggest that Scheiwer & Sklar gives the best results. Experimental Results ascertain that the proposed approach for the score level fusion outperforms the state-of-art.

Fuzzy Fusion for Multimodal Biometric

2013 ◽  
pp. 98-111
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Mathematical Tool ◽  
Fusion Method ◽  
Biometric System ◽  
Multimodal Biometric System ◽  
Noisy Input ◽  
Fuzzy Fusion ◽  
Biometric Information ◽  
Fusion Approach

Fuzzy logic is a mathematical tool that can provide a simple way to derive a conclusion with the presence of noisy input information. It is a powerful intelligent tool and used heavily in many cognitive and decision-making systems. In this chapter, fuzzy logic-based fusion approach for multimodal biometric system is discussed. After discussing the basics of fuzzy logic, the fuzzy fusion mechanism in the context of a multimodal biometric system is illustrated. A brief discussion on the research conducted for fuzzy logic-based fusion in different application domains is also presented. The biggest advantage of the system is that instead of binary “Yes”/“No” decision, the probability of a match and confidence level can be obtained. A fuzzy fusion-based biometric system can be easily adjusted by controlling weight assignment and fuzzy rules to fit changing conditions. Some results of experimentations conducted in a recent research investigation on two virtual multimodal databases are presented. The discussion on the effect of incorporating soft biometric information with the fuzzy fusion method to make the system more accurate and robust is also included.

Multimodal Biometric System and Information Fusion

2013 ◽  
pp. 48-68 ◽  
Keyword(s):  
Information Fusion ◽  
Fusion Method ◽  
Biometric System ◽  
Operational Parameters ◽  
Multimodal Biometric System ◽  
Biometric Systems ◽  
Fuzzy Fusion ◽  
Different Sources ◽  
Multimodal Biometric Systems

Integrating different information originating from different sources, known as information fusion, is one of the main factors of designing a biometric system involving more than one biometric source. In this chapter, various information fusion techniques in the context of multimodal biometric systems are discussed. Usually, the information in a multimodal biometric system can be combined in senor level, feature extraction level, match score level, rank level, and decision level. There is also another emerging fusion method, which is becoming popular—the fuzzy fusion. Fuzzy fusion deals with the quality of the inputs or with the quality of any system components. This chapter discusses the associated challenges related to making the choice of appropriate fusion method for the application domain, to balance between fully automated versus user defined operational parameters of the system and to take the decision on governing rules and weight assignment for fuzzy fusion.

scholarly journals Integration of various scales for measurement of insomnia

2021 ◽  
pp. 263208432110100
Author(s):  
Satyendra Nath Chakrabartty
Keyword(s):  
Test Scores ◽  
Data Driven ◽  
Weighted Sum ◽  
Future Studies ◽  
Normal Probability ◽  
Equivalent Test ◽  
Item Scores ◽  
Using Data ◽  
Set Up ◽  
Different Levels

Background Scales for evaluating insomnia differ in number of items, response format, and result in different scores distributions and score ranges and may not facilitate meaningful comparisons. Objectives Transform ordinal item-scores of three scales of insomnia to continuous, equidistant, monotonic, normally distributed scores, avoiding limitations of summative scoring of Likert scales. Methods Equidistant item-scores by weighted sum using data-driven weights to different levels of different items, considering cell frequencies of Item-Levels matrix, followed by normalization and conversion to [1, 10]. Equivalent test-scores (as sum of transformed item- scores) for a pair of scales were found by Normal Probability curves. Empirical illustration given. Results Transformed test-scores are continuous, monotonic and followed Normal distribution with no outliers and tied scores. Such test-scores facilitate ranking, better classification and meaningful comparison of scales of different lengths and formats and finding equivalent score combinations of two scales. For a given value of transformed test-score of a scale, easy alternate method avoiding integration proposed to find equivalent scores of another scales. Equivalent scores of scales help to relate various cut-off scores of different scales and uniformity in interpretations. Integration of various scales of insomnia is achieved by finding one-to-one correspondence among the equivalent score of various scales with correlation over 0.99 Conclusion Resultant test-scores facilitated undertaking analysis in parametric set up. Considering the theoretical advantages including meaningfulness of operations, better comparison, use of such method of transforming scores of Likert items/test is recommended test and items, Future studies were suggested.

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