Social Behavioral Information Fusion in Multimodal Biometrics

Sultana, Madeena; Paul, Padma Polash; Gavrilova, Marina L.

doi:10.1109/tsmc.2017.2690321

Cited by 40 publications

(27 citation statements)

References 38 publications

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“…To achieve desired authentication, the author introduced a system based on social behavior information fused with traditional biometric traits such as face and ear .The person behavior is extracted from one of the most popular online social network twitter and monitored the patterns like Re-tweet, Hash tag, URLS and Replies are analyzed weekly, monthly are fused with the PCA based feature extraction of face and ear. The system performance is better than the traditional biometric [5].2D log-Gabor filter is used to extract the information of face and iris (left and right) and Spectral Regression Kernel Discriminant Analysis (SRKDA) is used to reduce the feature dimension .The significant feature level fusion is used to fuse face and iris features .In order to match the test and trained features the Euclidean distance is used to measure the distance and decision about acceptance or rejection is drawn [6]. Multimodal biometric authentication based on weighted hybrid fusion of ear print, fingerprint, palm print fusion features (FF) and unimodal palm print features (UF).…”

Section: Introductionmentioning

confidence: 92%

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

Gowda¹,

AN²

2019

IJITEE

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The proposed work is a multimodal biometric authentication approach with image texture feature dimension reduction of trained feature vector which leads reduction in memory size and in turn reduces the computational time. In this paper hand and face features are used for person identification. The texture features of hand image are extracted using Haar and several Daubechie’s of 2D-DWT followed by 2D- edge detector gives better identification with reduction in feature vector and face features are extracted by neighborhood common characterization with block based segmentation approach to estimate the disparity in face. The neighborhood common characterization based structure recognition with a person representative per sample is more effective. The neighborhood features are constructed by extracting the similar blocks in the image, the intra pixel disparity feature is obtained by exploiting external common images to estimate the feasible facial disparities. Neighborhood common characterization reduces the overall residual of the given features over the local feature, common disparity dictionary, and shape based residual of a block. Neighbourhood common characterization representation, of face recognize with one representative per person more effectively. The system uses either of the biometric traits for person identification with 99.98% of authentication rate.

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Section: Introductionmentioning

confidence: 92%

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

Gowda¹,

AN²

2019

IJITEE

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“…Sankaran et al [137] proposed a Siamese convolutional neural network which utilized meta-data of face images such as yaw, pitch, and face size to enhance face recognition. Sultana et al [138] proposed incorporating social behavioral information extracted from online social networks in a multi-modal system based on face and ear recognition. Scores of different modalities were fused at the score-level in order to obtain the final decision.…”

Section: Year Authors Descriptionmentioning

confidence: 99%

A comprehensive overview of biometric fusion

2019

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The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of singlebiometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

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“…If any significant variation is observed, the system could take action for a possible intrusion. Sultana et al [119] combined social behavioral information of individuals that was extracted from the online social networks to fuse with traditional face and ear biometrics, to enhance the performance of the traditional biometric systems.…”

Section: Something You Are: Behavioral Biometrics Behavioral Biometricsmentioning

confidence: 99%

“…Authentication types Usability pros and cons indicated Security solutions or concerns reported Touch [9,113]; keystroke [115]; hold [8]; gait [116][117][118]; behavior profiling [119] Adaptive; continuous; multimodal;…”

Section: Modalitiesmentioning

confidence: 99%

“…risk-based; transparent [3,5,10,113,[119][120][121] [8,12,29,112,113,[115][116][117][122][123][124][125][126][127] end], P, auth}" holding of 3 prime attributes, where "begin" is authorization start date, "end" is either the constant ∞, or a deauthorization date after the start date, "P" is the duration of a session, and "auth" is an authorization function. Feng et al [4] determined that periodic authentication or automatic logouts are more detrimental while one-shot authentication solutions are prone to a wide variety of attacks.…”

Section: Modalitiesmentioning

confidence: 99%

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Demystifying Authentication Concepts in Smartphones: Ways and Types to Secure Access

Gupta

Buriro

Crispo

2018

Mobile Information Systems

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Smartphones are the most popular and widespread personal devices. Apart from their conventional use, that is, calling and texting, they have also been used to perform multiple security sensitive activities, such as online banking and shopping, social networking, taking pictures, and e-mailing. On a positive side, smartphones have improved the quality of life by providing multiple services that users desire, for example, anytime-anywhere computing. However, on the other side, they also pose security and privacy threats to the users' stored data. User authentication is the first line of defense to prevent unauthorized access to the smartphone. Several authentication schemes have been proposed over the years; however, their presentation might be perplexing to the new researchers to this domain, under the shade of several buzzwords, for example, active, continuous, implicit, static, and transparent, being introduced in academic papers without comprehensive description. Moreover, most of the reported authentication solutions were evaluated mainly in terms of accuracy, overlooking a very important aspect-the usability. is paper surveys various types and ways of authentication, designed and developed primarily to secure the access to smartphones and attempts to clarify correlated buzzwords, with the motivation to assist new researchers in understanding the gist behind those concepts. We also present the assessment of existing user authentication schemes exhibiting their security and usability issues.

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Social Behavioral Information Fusion in Multimodal Biometrics

Cited by 40 publications

References 38 publications

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

Dimension Reduction of Hand and Face Feature Level Fusion in Multimodal Biometric Authentication

A comprehensive overview of biometric fusion

Demystifying Authentication Concepts in Smartphones: Ways and Types to Secure Access

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