When Faces Are Combined with Palmprints: A Novel Biometric Fusion Strategy

Feng, Guiyu; Dong, Kaifeng; Hu, Dewen; Zhang, David

doi:10.1007/978-3-540-25948-0_95

Cited by 55 publications

(39 citation statements)

References 13 publications

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“…For example, iris recognition suffers from some problems like camera distance, eyelids and eyelashes occlusion, lenses, and reflections [18][19][20]. Face changes overages and unstable, and twins may have similar face features.…”

Section: Unimodal Biometrics Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Unimodal Biometrics Limitationsmentioning

confidence: 99%

“…For example, in year 2004, Feng et al [19] developed a feature level fusion based multimodal biometric system using face and palm print. They used Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as classification algorithms.…”

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

View full text Add to dashboard Cite

show abstract

“…In research by Feng et al [32], face and palm-print were fused at the feature level using PCA and ICA features. Their results in a validation framework were an indicator of superiority of ICA in feature level fusion.…”

Section: Feature Level Fusionmentioning

confidence: 99%

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

Talebi

Gavrilova

2015

Computer Analysis of Images and Patterns

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In recent years, the inevitable need for reliable biometric identity management systems in applications such as border crossing, welfare distribution, and accessing critical facilities has drawn researchers' attention to the area of biometric. The intrinsic limitations of unimodal biometric systems such as non-universality, sensitivity to noisy sensor data, inter and intra class variations and spoof attacks have resulted in significant attention toward multimodal biometric systems. An important aspect of a multimodal biometric system is the fusion of information from multiple biometric sources. This thesis focuses on using the notion of Resemblance Probability Distributions to calculate confidence measures for different biometric matchers and use these confidence measures in the fusion module to improve the identification rate of the system. This thesis approaches the problem of low inter class variation and low quality data by proposing Rank List Reinforcement and Confidence-based Ranked List Selection methods.

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“…They dealt with the multimodal biometric decision fusion problem as a two-stage problem that includes learning and decision. While, face and palmprint were combined by Feng et al [36] through concatenated the features extracted by using PCA and ICA with the nearest neighbour classifier (nnc) and SVM as the classifier. Snelick et al [37] proposed a multimodal approach for face and fingerprint, with fusion methods at the score level through employing three fingerprint recognition commercial systems and one face recognition commercial system.…”

Section: Related Workmentioning

confidence: 99%

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

ElAlami¹,

Amin²,

El-alfi³

2012

IJCA

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Biometric based person identity verification is gaining more and more attention. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and reliability compared with recognition using a single biometric. The present paper introduces a new personal identification framework that is based on the fusion of face and fingerprint biometrics. The proposed framework overcomes the limitations of face recognition systems as well as fingerprint verification systems. The gray-level co-occurrence matrix and the minutiae extraction are used to represent the features of face and fingerprint image respectively. This framework uses the correlation coefficient as a similarity measure to retrieve the closest face and the corresponding fingerprint images with a query image. Experimental results performed on a given database of face and fingerprint images show that the proposed framework improved greatly the security and recognition rate.

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When Faces Are Combined with Palmprints: A Novel Biometric Fusion Strategy

Cited by 55 publications

References 13 publications

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

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

Confidence Based Rank Level Fusion for Multimodal Biometric Systems

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

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