FES: A System for Combining Face, Ear and Signature Biometrics Using Rank Level Fusion

Monwar, Md. Maruf; Gavrilova, Marina L.

doi:10.1109/itng.2008.254

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…Rank(1) CIP of 98.5% is reported for markov chain method. Monawar et al [24] have consolidated rank information of face, ear and signature matchers using logistic regression and Borda count methods. Authors have modified both the methods by considering the top ranks identities that appear in two rank lists out of three rank lists of face, ear and signature matchers.…”

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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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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“…They reported a 97% rank-1 recognition rate based on a subset of 85 subjects from the UND 3D databases. Monwar and Gavrilova [2008] developed a multimodal biometric system that used face, ear, and signature features extracted by PCA or Fisher's linear discriminant methods. The fusion is conducted at rank level.…”

Section: Face Profile and Earmentioning

confidence: 99%

A survey on ear biometrics

et al. 2013

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Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other noncontact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion, earprint forensics, ear symmetry, ear classification, and ear individuality.This article provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers.

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“…Rank-level fusion [17,18] is a parallel ensemble that is used when the output of a matcher is the rank of the candidates in a template database. Unlike a verification system, an identification system can output a rank or a candidate list instead of a match score or a Boolean value.…”

Section: Hybrid Ensemble Frameworkmentioning

confidence: 99%

A hybrid biometric identification framework for high security applications

Yin

Ning

et al. 2014

Front. Comput. Sci.

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Research on biometrics for high security applications has not attracted as much attention as civilian or forensic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analysis and identification of the problems to be solved in order to meet the performance requirements for high security applications, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false acceptance rate (FAR) and then use the hybrid ensemble framework makes the false reject rate (FRR) as low as possible. Three experiments are performed to verify the effectiveness and generalization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is significantly lower than other state of the art methods. Second, in face verification, the framework also results in a large reduction in incorrect classification. Finally, assessing the performance of the framework on a combination of face and gait verification using a heterogeneous database show this framework can achieve both 0% false rejection and 0% false acceptance simultaneously.

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FES: A System for Combining Face, Ear and Signature Biometrics Using Rank Level Fusion

Cited by 31 publications

References 17 publications

Feature and Decision Level Fusion in Children Multimodal Biometrics

Feature and Decision Level Fusion in Children Multimodal Biometrics

A survey on ear biometrics

A hybrid biometric identification framework for high security applications

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