Optimal Face-Iris Multimodal Fusion Scheme

Sharifi, Omid; Eskandari, Maryam

doi:10.3390/sym8060048

Cited by 29 publications

(25 citation statements)

References 32 publications

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“…FAR = 0,00001%, FRR = 0.1% [52] Retina [53,54] The true acceptance rate 98.148% [55] Face [56,57] FAR = 0,1%, FRR = 7% [52] Keystroke dynamics [58][59][60] Classification accuracy 92.60% [61] Signature dynamics [62,63] Average FAR = 5.125%, FRR = 5.5%, AER = 5.31% [64] Speech [65][66][67] Classification accuracy up to 99%. EER = 1% [68] The analysis of the presented values of the accuracy of authentication does not allow us to speak of a single use of features, however, it makes relevant their use within multimodal authentication (for example, face + iris [69], face and vein arrangement on finger, fingerprint, and voice [70], complex parameters of fingers and palms [71,72]) and the construction of ensembles of various types [73,74].…”

Section: Biometric Characteristic Papers Resultsmentioning

confidence: 99%

Information Security Methods—Modern Research Directions

et al. 2019

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In Tomsk University of Control Systems and Radioelectronics (TUSUR) one of the main areas of research is information security. The work is carried out by a scientific group under the guidance of Professor Shelupanov. One of the directions is the development of a comprehensive approach to assessing the security of the information systems. This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them. The main directions of information security tools development are dynamic methods of biometrics, methods for generating prime numbers for data encryption, steganography, methods and means of data protection in Internet of Things (IoT) systems. The article presents the main results of research in the listed areas of information security. The resultant properties in symmetric cryptography are based on the properties of the power of the generating functions. The authors have obtained symmetric principles for the development of primality testing algorithms, as discussed in the Appendix.Symmetry 2018, 10, x FOR PEER REVIEW 3 of 33 Ported to AMR system devices, IPsec ensures mutual authentication of network devices using the IKEv2 protocol. Optionally, the network can be configured based on the EAP-PSK protocol. During configuration, the devices receive network addresses and authentication keys, at which point the execution of EAP-PSK is stopped and data is transferred via IPsec. Another option is to use pre-installed certificates on the devices. In this case, the initial configuration is done manually, but the network does not require EAP-PSK to be used.Data integrity control and encryption during transmission are provided by ESP, which is the protocol used in IPsec at the transport level. This protocol ensures the security of both the data transmitted and packet headers at the network level.This approach makes it possible to ensure reliable authentication of the AMR system devices and the security of the data to be transmitted and opens a wide range of options for the configuration of network operation; however, it cannot be used in networks with heterogeneous communication channels. The EAP-PSK-based approach offers less flexibility but is suitable for networks with heterogeneous communication channels. For an AMR system, a list of threats was proposed based on the developed methodology. Threats to the confidentiality of the system are threats related to the collection of information about the system. This can be a list of devices, software versions, authentication data, access control policies, network addresses, interaction protocols, etc. Threats to the integrity of the automated system for commercial accounting are: substitution of an object, substitution of a communication channel, deletion of an object, destruction of a communication channel, addition of an unauthorized object, creation of an unauthorized communication channel; change of communication channel or object settings...

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Section: Biometric Characteristic Papers Resultsmentioning

confidence: 99%

Information Security Methods—Modern Research Directions

et al. 2019

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“…Finally, the threshold-optimized approach was used to implement decision level fusion where it improved the final decision. By computing the FAR values, the system performance acquired 94.91%, 95.00%, and 96.87% for fusion in feature, score, and decision level respectively [9].…”

Section: Related Workmentioning

confidence: 99%

“…The operation of LBP depends on the eight neighbors of the present pixel, where the center pixel is used as a threshold for its neighbors using 7and (8). The final code of center pixel is generated by combined the binary coding of its eight neighbors using (9). The LBP operation could be expanded to include different number of neighbors by generating a circle of neighbor points around the center pixel with radius equals to .…”

Section: Lbpmentioning

confidence: 99%

Multimodal Biometric System based Face-Iris Feature Level Fusion

Hamd¹,

Mohammed²

2019

IJMECS

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This paper proposed feature level fusion technique to develop a robust multimodal human identification system. The humane face-iris traits are fused together to improve system accuracy in recognizing 40 persons taken from ORL and CASIA-V1 database. Also, low quality iris images of MMU-1 database are considered in this proposal for further test of recognition accuracy. The face-iris features are extracted using four comparative methods. The texture analysis methods like Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are both gained 100% accuracy rate, while the Principle Component Analysis (PCA) and Fourier Descriptors (FDs) methods achieved 97.5% accuracy rate only.

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“…Currently, the use of biometric recognition systems in term of identification and/or verification of individuals according to their physical or behavioral characteristics is extensively studied in situations with high security demands [1][2][3][4][5][6][7][8][9][10]. In fact, the ability of biometrics to improve recognition performance and security of applications considered as increasing interest of researchers compared to conventional techniques such as token-based and knowledge-based methods.…”

Section: Introductionmentioning

confidence: 99%

Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

Sharifi¹

2019

IJIGSP

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Recognition performance of biometric systems is affected through spoofing attacks made by fake identities. The focus of this paper is on presenting a new scheme based on score level and decision level fusion to monitor individuals in term of real and fake. The proposed fake detection scheme involve consideration of both handcrafted and deep learned techniques on face images to differentiate real and fake individuals. In this approach, convolutional neural network (CNN) and overlapped histograms of local binary patterns (OVLBP) methods is used to extract facial features of images. The produced matching scores provided by CNN and OVLBP then combined to form a fused score vector. Finally, the last decision on real and attack images is done by combining decisions of hybrid scheme using majority vote of CNN, OVLBP and their fused vector. Experimental results on public spoof databases such as Print-Attack and Replay-Attack face databases demonstrate the strength of the proposed anti-spoofing method for fake detection.

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Optimal Face-Iris Multimodal Fusion Scheme

Cited by 29 publications

References 32 publications

Information Security Methods—Modern Research Directions

Information Security Methods—Modern Research Directions

Multimodal Biometric System based Face-Iris Feature Level Fusion

Score-Level-based Face Anti-Spoofing System Using Handcrafted and Deep Learned Characteristics

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