A robust face and ear based multimodal biometric system using sparse representation

Huang, Zengxi; Liu, Yiguang; Li, Chunguang; Yang, Menglong; Chen, Liping

doi:10.1016/j.patcog.2013.01.022

Cited by 73 publications

(47 citation statements)

References 31 publications

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“…Due to the unavailability of proper datasets for multimodal recognition studies [2], often virtual synthetically obtained by pairing modalities of different subjects from different databases. To the best of our knowledge, the proposed approach is the first study where are extracted from a single data source that belongs to the same subject.…”

Section: System Block Diagram: Multimodal Biometrics Recognition Frommentioning

confidence: 99%

Multimodal Biometrics Recognition from Facial Video via Deep Learning

Maity

Abdel-Mottaleb

Asfour

2017

SIPIJ

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Section: System Block Diagram: Multimodal Biometrics Recognition Frommentioning

confidence: 99%

Multimodal Biometrics Recognition from Facial Video via Deep Learning

Maity

Abdel-Mottaleb

Asfour

2017

SIPIJ

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“…Moreover, ICA-based accuracy rate was (85%, 92.5%) for (face, palm print), while 99.17% after fusion. some previous fused modalities based on feature level fusion as in [35][36][37][38][39][40][41].…”

Section: ) Feature Level Fusionmentioning

confidence: 99%

Multi-Biometric Systems: A State of the Art Survey and Research Directions

Gad¹,

El-Fishawy²,

El‐Sayed³

et al. 2015

ijacsa

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Abstract-Multi-biometrics is an exciting and interesting research topic. It is used to recognizing individuals for security purposes; to increase security levels. The recent research trends toward next biometrics generation in real-time applications. Also, integration of biometrics solves some of unimodal system limitations. However, design and evaluation of such systems raises many issues and trade-offs. A state of the art survey of multi-biometrics benefits, limitations, integration strategies, and fusion levels are discussed in this paper. Finally, upon reviewing multi-biometrics approaches and techniques; some open points are suggested to be considered as a future research point of interest.

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“…[11] identified a new geometrical feature Width Centroid Contour Distance for finger geometry biometric. [12] developed a face and ear biometric system which uses a feature weighing scheme called Sparse Coding error ratio. [13]proposed the fusion method based on a compressive sensing theory which contains over complete dictionary, an algorithm for sparse vector approximation and fusion rule.…”

Section: Literature Reviewmentioning

confidence: 99%

PSO Watermarking Model for Multimodal Biometric System

Nair¹,

Aruna²

2014

IJCA

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Multimodal Biometric Watermarking System using multiple sources of information for establishing the individuality has been widely recognized, computational models for multimodal biometrics recognition have only recently got attention. In this paper multimodal biometric images such as fingerprint, palmprint, and iris are extracted individually and are fused together using Average, Minimum and Maximum fusion mechanism. The fused template is then watermarked using the PSO watermarking system. The biometric features used here are fingerprint, iris and palmprint. The image quality is measured by using various metrics such as Peak Signal Noise ratio(PSNR), Normalized Absolute Error(NAE) and Normalized Cross Correlation(NCC). CASIA database is chosen for the biometric images. All the images are 8 bit graylevel JPEG image with the resolution of 320*280.

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A robust face and ear based multimodal biometric system using sparse representation

Cited by 73 publications

References 31 publications

Multimodal Biometrics Recognition from Facial Video via Deep Learning

Multimodal Biometrics Recognition from Facial Video via Deep Learning

Multi-Biometric Systems: A State of the Art Survey and Research Directions

PSO Watermarking Model for Multimodal Biometric System

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