scholarly journals A Robust Multimodel Biometric Crypto System

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1953-1961
Keyword(s):  
Final Decision ◽  
Key Generation ◽  
Biometric System ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Aes Algorithm ◽  
Behavioural Characteristics ◽  
Biometric Systems ◽  
Biometric Templates ◽  
Level Fusion

Biometric based authentication has several advantages over traditional password or PIN based authentication process because biometric is consists of physical or behavioural characteristics i.e fingerprint, face, Finger Knuckle Print (FKP), iris, voice etc. Unimodal biometric system h as some drawbacks i.e non universality, inter-class variation, intra-class variation; system can be circumvented by the skilled imposter etc. These drawbacks can overcome by multimodal biometric system as it combines more than one modality for authentication. When multimodal system combined with cryptography it makes system more robust and secure. In this paper, a robust multimodal biometric crypto system has been proposed, in which two modalities (FKP and face) are used for authentication of a person and one modality (fingerprint) is used for key generation. AES algorithm with fingerprint based key is used for securing the biometric templates. At authentication time, decision level fusion with AND rule is used for making the final decision. The proposed multimodal biometric crypto system is more robust and secure as compare with other multimodal biometric systems. Experimental results are shown with the help of MATLAB3. 2017b.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

Multimodal of Ear and Face Biometric Recognition Using AARK Threshold segmentation and Classifier with Score level fusion

2021 ◽  
Author(s):  
SANTHAM BHARATHY ALAGARSAMY ◽  
Kalpana Murugan
Keyword(s):  
Projection Data ◽  
Biometric System ◽  
Biometric Recognition ◽  
Data Set ◽  
Score Level Fusion ◽  
Multimodal Biometric System ◽  
The Individual ◽  
Individual Scores ◽  
Face Biometrics ◽  
Level Fusion

Abstract More than one biometric methodology of an individual is utilized by a multimodal biometric system to moderate a portion of the impediments of a unimodal biometric system and upgrade its precision, security, and so forth. In this paper, an incorporated multimodal biometric system has proposed for the identification of people utilizing ear and face as input and pre-preparing, ring projection, data standardization, AARK limit division, extraction of DWT highlights and classifiers are utilized. Afterward, singular matches gathered from the different modalities produce the individual scores. The proposed framework indicated got brings about the investigations than singular ear and face biometrics tried. To certify the individual as genuine or an impostor, the eventual outcomes are then utilized. On the IIT Delhi ear information base and ORL face data set, the proposed framework has checked and indicated an individual exactness of 96.24%

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.

Development of Face and Signature Fusion Technology for Biometrics Authentication

2018 ◽  
Vol 6 (9) ◽  
pp. 61 ◽  
Author(s):  
Krishna Shinde ◽  
Sumegh Tharewal
Keyword(s):  
Physiological Model ◽  
Information Technology Industry ◽  
Fusion Technology ◽  
Biometric System ◽  
Score Level Fusion ◽  
Multimodal Biometric System ◽  
Technology Industry ◽  
Authentication And Authorization ◽  
Signature Recognition ◽  
Level Fusion

The Biometrics system is getting popularity since last decade As per Information Technology industry demand. This techn-ology are satisfy authentication and authorization process  needs. But the  unimodal biometric system  have own limitations. the limitation of unimodal, we can choosing the  approach of multimodal biometric system. In this research paper choose the physiological model for face recognition and behavioural model for signature recognition. The recognition of face and signature used match score level fusion. In this fusion technology for secured authentication of person

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