Multimodal Belief Fusion for Face and Ear Biometrics

Kisku, Dakshina Ranjan; Gupta, Phalguni; Mehrotra, Hunny; Sing, Jamuna Kanta

doi:10.4236/iim.2009.13024

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…e modified-Vote rule is reported slightly inferior to score-level fusion schemes such as Sum and Product rules, while it is comparable to the Median-rule. Kisku et al [38] apply two trained Gaussian Mixture Models (GMM) to estimate the match score distributions of Gabor features of the face and ear, respectively, and, then, verify an identity based on the Dempster-Shafer theory of evidence. ey get an EER of 4.47% on the IIT Kanpur multimodal database, having 400 subjects in total.…”

Section: Decision-level Fusion Methods Rahman and Ishikawamentioning

confidence: 99%

An Overview of Multimodal Biometrics Using the Face and Ear

Huang

Wang

et al. 2020

Mathematical Problems in Engineering

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In the recent years, we have witnessed the rapid development of face recognition, though it is still plagued by variations such as facial expressions, pose, and occlusion. In contrast to the face, the ear has a stable 3D structure and is nearly unaffected by aging and expression changes. Both the face and ear can be captured from a distance and in a nonintrusive manner, which makes them applicable to a wider range of application domains. Together with their physiological structure and location, the ear can readily serve as supplement to the face for biometric recognition. It has been a trend to combine the face and ear to develop nonintrusive multimodal recognition for improved accuracy, robustness, and security. However, when either the face or the ear suffers from data degeneration, if the fusion rule is fixed or with inferior flexibility, a multimodal system may perform worse than the unimodal system using only the modality with better quality sample. The biometric quality-based adaptive fusion is an avenue to address this issue. In this paper, we present an overview of the literature about multimodal biometrics using the face and ear. All the approaches are classified into categories according to their fusion levels. In the end, we pay particular attention to an adaptive multimodal identification system, which adopts a general biometric quality assessment (BQA) method and dynamically integrates the face and ear via sparse representation. Apart from a refinement of the BQA and fusion weights selection, we extend the experiments for a more thorough evaluation by using more datasets and more types of image degeneration.

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Section: Decision-level Fusion Methods Rahman and Ishikawamentioning

confidence: 99%

An Overview of Multimodal Biometrics Using the Face and Ear

Huang

Wang

et al. 2020

Mathematical Problems in Engineering

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“…For other types of facial expressions, they achieved 98.1% and 96.83% identification and verification rates, respectively. Kisku et al [2009b] used Gabor filters to extract features of landmarked images of face and ear. They used a locally collected database of 1600 images from 400 subjects.…”

Section: Frontal Face 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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“…To deal with multimodal biometric recognition, feature fusion has become a very important research aspect [4]- [12], because fusing different types of features provides complementary information. Most of the recent human recognition works [10], [13-[17] utilized feature level fusion to overcome the challenges of constrained resources, and to increase the system security and system performance. The well-known traditional feature fusion methods involve serial and parallel fusions.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, recognition metrics have been considered as an important research point for the development of multimodal biometric recognition systems. To the best of our knowledge, most of the previous works [4], [9], [10], [13], [15], [16] have adopted traditional distance metrics and classifiers. Due to the number of images per person that are usually limited to 3∼5 images and with noises, traditional distance metrics and classifiers cannot achieve the performance desired.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

et al. 2021

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Metric learning has significantly improved machine learning applications such as face re-identification and image classification using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. However, to the best of our knowledge, it has not been investigated yet, especially for the multimodal biometric recognition problem in immigration, forensic and surveillance applications with uncontrolled ear datasets. Therefore, it is interesting and very attractive to propose a novel framework for multimodal biometric recognition based on Learning Distance Metric (LDM) via kernel SVM. This paper considers metric learning for SVM by investigating a hybrid Learning Distance Metric and Directed Acyclic Graph SVM (LDM-DAGSVM) model for multimodal biometric recognition, where LDM and DAGSVM are two emerging techniques in dealing with classification problems. Different from existing multimodal biometric recognition methods, the proposed approach aims to learn Mahalanobis distance metric via kernel SVM to maximize the inter-class variations and minimize the intra-class variations, simultaneously. Experimental results on the uncontrolled datasets such as AR face and AWE ear datasets show that the proposed approach achieves competitive performance compared with models working on individual modalities and overperforms the state-of-the-art multimodal methods. The proposed model achieves fivefold classification accuracy around 99.85 % for the face and ear images. INDEX TERMSBiometrics, multimodal biometrics, face and ear images, Mahalanobis distance, metric learning, DAGSVM. AHMED HAGAG (Member, IEEE) received the B.Sc. degree (Hons.

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Multimodal Belief Fusion for Face and Ear Biometrics

Cited by 16 publications

References 12 publications

An Overview of Multimodal Biometrics Using the Face and Ear

An Overview of Multimodal Biometrics Using the Face and Ear

A survey on ear biometrics

A Hybrid Model Combining Learning Distance Metric and DAG Support Vector Machine for Multimodal Biometric Recognition

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