Markov chain model for multimodal biometric rank fusion

Monwar, Md. Maruf; Gavrilova, Marina L.

doi:10.1007/s11760-011-0226-8

Cited by 41 publications

(10 citation statements)

References 25 publications

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“…A comparative study on different rank level fusion algorithms were conducted in this research. Authors integrated face, iris and ear biometric features using Markov chain model based rank level fusion method in [29]. In [30], fingerprint and gait biometrics were fused using contourlet derivative weighted rank level fusion method.…”

Section: Literature Reviewmentioning

confidence: 99%

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Tumpa

Gavrilova

2020

IEEE Access

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The goal of a biometric system is to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB), being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users' writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on users' writing profiles, reply, retweet, shared weblink, trendy topic networks and temporal profiles is proposed. Score and rank level weighted fusion algorithm performance is compared on a social interaction database of 241 Twitter users. The experimental results establish that the users' writing profiles have the highest impact over other social biometric features and that score level fusion algorithms perform better than rank level fusion on SBB. The proposed system has achieved recognition rate of 99.45% at rank-1 after cross-validation using genetic algorithm based score level fusion algorithm. The system outperformed all prior researches on SBB in terms of identification accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Tumpa

Gavrilova

2020

IEEE Access

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“…Other score level fusion schemes proposed in the literature are reported in [20][21][22]. Monawar et al [23] have proposed a markov chain method for consolidating the rank information of face, iris and ear modality matchers. Rank(1) CIP of 98.5% is reported for markov chain method.…”

Section: Feature and Decision Level Fusion In Children Multimodal Biomentioning

confidence: 99%

Feature and Decision Level Fusion in Children Multimodal Biometrics

D. N*.,

H. K.

2020

IJRTE

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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.

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“…Abaza and Ross [11] proposed quality based rank fusion methods. Other rank level fusion techniques are Bucklin Majority Voting [12], Bayesian approach [13], Nonlinear Weighted Rank [14], Markov Chain based fusion [15], Fuzzy Rank based fusion [16] and Neural Network based fusion methods [17]. Various other rank level fusion techniques proposed in the fields of image, document and information retrieval are: Inverse Rank Position [18], Leave Out [18], Reciprocal Rank Fusion [20] and Inverse Square Rank [21].…”

Section: A Related Workmentioning

confidence: 99%

Rank level fusion in multibiometric systems

Sharma

Das

Joshi

2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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Multibiometric systems have recently become a preferred option for human identification over the unibiometric systems. It increases the recognition rate and confidence in the final decision, and simultaneously reduces the failure to enroll rate (FER). For identification mode, rank level fusion is a feasible option as incompatibility and normalization issues present at the score level fusion are not prominent at this level and also sufficient information is present to fuse as opposed to the decision level fusion. We propose an improvement in existing rank level fusion techniques using two levels of hierarchy. Series and parallel combinations are proposed to combine the output of various rank level fusion techniques. Two formulations of series and parallel combinations are extensively evaluated on multi-algorithm, multiinstance and multi-modal biometric systems created from three publicly available datasets: (i) NIST BSSR1 [1] multimodal biometric score database, (ii) Face Recognition Grand Challenge V2.0 [2] and (iii) LG4000 [3] iris images.

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Markov chain model for multimodal biometric rank fusion

Cited by 41 publications

References 25 publications

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Feature and Decision Level Fusion in Children Multimodal Biometrics

Rank level fusion in multibiometric systems

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