Multimodal biometric fusion of face and palmprint at various levels

Noushath, S.; Imran, Mohammed; Jetly, K.; Rao, Ashok; Kumar, G. Hemantha

doi:10.1109/icacci.2013.6637453

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…The justifiable reason of combining the two or more modalities is to improve the recognition accuracy (Aggarwal and Gulati, 2012) and overcome other limitations of single biometric systems (Nandakumar et al, 2008). In general (Imran et al, 2013) the fusion process enhances the system precision significantly which simply endorses well established fact about multimodal system.…”

Section: Introductionmentioning

confidence: 99%

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A Proposed Integrated Human Recognition for Security Reassurance

Sufyanu¹,

Mohamad²,

Ben-Musa³

2015

American Journal of Applied Sciences

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A lot of systems require reliable and thorough authentication to ratify individual's claimed identity, especially in national and international security and defense. Unibiometric system suffers inherent weaknesses that are unavoidable in the system. Such inadequacies may directly or indirectly lead to an unacceptable error. For this reason, researchers pay great attention to the more advanced biometrics (referred to as multimodal biometrics). A multimodal biometric system involves at least two unimodal traits in a sole identification. This alleviates some of the drawbacks and improves recognition accuracy despite number of population considered. This research proclaims a new technique of integration for human recognition improvement using four physiological characteristics: Face, iris, palmprint and thumbprint. In addition, two fusion strategies namely, score-level and decision-level are presented using robust algorithms. The multimodal biometric systems possessed relative merits or pluses over its counterpart monomodal biometrics. The higher biometric modalities are integrated the more system is secured, hence better security assurance. Therefore, successfully conducting our research can derive supreme benefit over conventional multimodal biometrics. The integration results to outstanding gap coverage for security reassurance.

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Section: Introductionmentioning

confidence: 99%

“…Feature Extraction Level: Fusion at the feature level concatenates the features extracted using two or more modalities (Imran et al, 2013). Matching Score Level: Fusion at the matching score level combines the scores which describes the similarities of biometric traits acquired and their templates obtained by each biometric system.…”

Section: Introductionmentioning

confidence: 99%

A Proposed Integrated Human Recognition for Security Reassurance

Sufyanu¹,

Mohamad²,

Ben-Musa³

2015

American Journal of Applied Sciences

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“…S Noushath et al [2] have proposed the multimodal biometrics based on face and palmprint. The fusion of this system was integrated at four different levels and the results were compared against each level.…”

Section: Related Workmentioning

confidence: 99%

“…In [5] the authors have proposed the FisherFace for extracting the linear discriminate features. Noushath et al [6] have extracted the features of palm and face by using LDA algorithm respectively. Ahmed et al [7] have utilized the Gabor filter for extracting the discriminant features, then used Principal Component Analysis (PCA) to represent the data in class and Linear Discriminant Analysis (LDA) for discrimination the data between classes.…”

Section: Related Workmentioning

confidence: 99%

Multimodal of Face and Iris Based on Local Binary Pattern and Gabor-Zernike Moments

Alsubari

Ramteke

2018

ijarcs

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This paper presents the multimodal of face and iris based on the same feature extraction techniques for the both traits. For locating the iris, compute the next three neighbour-pixels from the pupil-circle in horizontal-right and horizontal-left, and if it found the value of those pixels are close to the sclera values (because it is close to the white color), then it will stop otherwise continue to the next pixels. The radius of the iris is obtained from the number of skipped-pixels. The iris is normalized by using the Rubber Sheet Normalization. The texture features of iris and face were extracted by using the Local Binary Pattern (LBP) and Gabor-Zernike Moments. Different classifications were used to evaluate the proposed system. The experiment was performed on the ORL and CASIA-Iris databases and the performance of the system is found to be satisfactory.

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“…S Noushath et al [5] proposed fusion of face and palmprint at the four levels and each level had difference techniques: at the sensor level used wavelets based image fusion scheme, at the feature level used few normalization techniques, at the score level used some rules of fusion such as sum, max and min rule to combine the matching score, finally at the score level used a logical AND & OR operator. After that, they compare the result of each level.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Alsubari¹,

Ramteke²

2017

IJCA

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This paper describes the recognition of multimodal biometric systems based on the face and palmprint features. The common feature extraction technique between the palmprint and face recognition system is the Local Binary Pattern. In the palmprint recognition, the region of palm was extracted form the entire hand. The Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) were used to extract the features of palm. The Zernike moments and LBP were used to extract the features of face. The features of palm and face were integrated as feature vector. To evaluated the accuracy of the system, different classifications were applied such as SVM, KNN, Linear Discriminant. This experiment was performed on the ORL and CASIA database. The proposed system was tested on the said database and found to be satisfactory.

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Multimodal biometric fusion of face and palmprint at various levels

Cited by 12 publications

References 15 publications

A Proposed Integrated Human Recognition for Security Reassurance

A Proposed Integrated Human Recognition for Security Reassurance

Multimodal of Face and Iris Based on Local Binary Pattern and Gabor-Zernike Moments

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

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