Research on Kernel-Based Feature Fusion Algorithm in Multimodal Recognition

Xu, Xiaona; Pan, Xiuqin; Zhao, Yue; Pu, Qiumei

doi:10.1109/itcs.2009.139

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…For the feature fusion method, the best performance was achieved by the Weightedsum rule at 94.52% accuracy. The Average rule recognition rate was at 96.41% and the Product rule recognition rate was at 96.20% [15]. The combination of 2DPCA and LDA achieved a higher recognition rate of 94.4% [18].…”

Section: Resultsmentioning

confidence: 99%

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Revisiting feature level and score level fusion techniques in multimodal biometrics system

Venkatesha

Radhika³

2012

2012 International Conference on Multimedia Computing and Systems

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The primary application of biometric technology is to analyze human characteristics for security purpose as security issues have been the focused interest in recent years. A survey of different fusion techniques in multimodal biometrics is made. This paper also attempts to identify some of the challenges and issues that confront research in multimodal biometrics. Keywords-Multi modal; feature level fusion; score level fusionI.

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Section: Resultsmentioning

confidence: 99%

“…The kernel based non-intrusive fusion of ear and face features were obtained by utilizing connection relationship [15,19]. To achieve the rights management of digital content with large population coverage, watermarking algorithm was used.…”

Section: I) Fusion At Feature Extraction Levelmentioning

confidence: 99%

Revisiting feature level and score level fusion techniques in multimodal biometrics system

Venkatesha

Radhika³

2012

2012 International Conference on Multimedia Computing and Systems

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“…Table 1 sums up the various highlights of multi-modular biometric considers. Xu Xiaona et al [7] propose a novel bit based component combination calculation approach in the mix of face and ear. The component combination approach was acquainted and applied multimodal biometrics dependent on the ear and profile face biometrics combination, in blend with the KPCA or KFDA calculation.…”

Section: Related Workmentioning

confidence: 99%

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

Alagarsamy

Murugan

2021

Preprint

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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%

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Research on Kernel-Based Feature Fusion Algorithm in Multimodal Recognition

Cited by 7 publications

References 10 publications

Revisiting feature level and score level fusion techniques in multimodal biometrics system

Revisiting feature level and score level fusion techniques in multimodal biometrics system

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

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