An adaptive bimodal recognition framework using sparse coding for face and ear

Huang, Zengxi; Liu, Yiguang; Li, Xuwei; Li, Jie

doi:10.1016/j.patrec.2014.10.009

Cited by 30 publications

(23 citation statements)

References 18 publications

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“…Though, the accuracy of this technique does not satisfy the requirement of many real-world applications, where it suffers from significant performance translation and rotation invariant. Huang et al (2015) introduced an adaptive bimodal sparse representation based on classification, that is, adaptive face and ear using bimodal recognition system based on sparse coding, where the qualities of weighted feature is selected. This system requires to pre-process each trait biometric before extracting the features.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

Cherrat

Alaoui

Bouzahir

2020

PeerJ Computer Science

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In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the region of interest image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the RF classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

Cherrat

Alaoui

Bouzahir

2020

PeerJ Computer Science

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show abstract

“…Though, the accuracy of this technique does not satisfy the requirement of many real-world applications, where it suffers from significant performance translation and rotation invariant. Huang et al (Huang et al, 2015) introduced an adaptive bimodal sparse representation based on classification, i.e. adaptive face and ear using bimodal recognition system based on sparse coding, where the qualities of weighted feature is selected.…”

Section: Literature Reviewmentioning

confidence: 99%

Peer Review #2 of "Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images (v0.1)"

2020

View full text Add to dashboard Cite

In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional Neural Network (CNN), Softmax and Random Forest classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied to separate the foreground and background region based on K-means and DBSCAN algorithm. Furthermore, the features are extracted using CNNs and dropout approach, after that, the Softmax performs as a recognizer. In conventional fingervein system, the Region of interest (ROI) image contrast enhancement using exposure fusion framework is input into the CNNs model. Moreover, the Random Forest (RF) classifier is proposed for classification. In conventional face system, the CNNs architecture and Softmax are required to generate face feature vectors and classify personal recognition. The score provided by these systems is combined for improving Human identification. The proposed algorithm is evaluated on publicly available SDUMLA-HMT real multimodal biometric database using a GPU based implementation. Experimental results on the datasets has shown significant capability for identification biometric system. The proposed work can offer an accurate and efficient matching compared with other system based on unimodal, bimodal, multimodal characteristics.

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“…One typical approach is to measure the quality of the signal extracted from a biometric sensor. For example, in face and ear biometric, the sparse coding error which can detect expression, pose and random pixel corruption has been proposed as a quality metric [13]. In speaker verification, signal-to-noise ratio (SNR) and the perceived quality of speech utterance have been used [38].…”

Section: Related Workmentioning

confidence: 99%

“…As a result, attackers could easily produce a signature that matches the template, resulting in a compromised account. In this context, an assessment of biometric template characteristics could be used to design proper mechanisms to cope with such weak templates [2,13,28,39]. For example, given a template that is predicted to yield high FAR (False Acceptance Rate: the likelihood that forgery samples will be incorrectly accepted by the system) but low FRR (False Rejection Rate: the likelihood that genuine samples will be incorrectly rejected by the system), the system could examine whether a few bad enrolled samples could be safely removed to lower FAR without degrading FRR.…”

Section: Introductionmentioning

confidence: 99%

Distinctiveness, complexity, and repeatability of online signature templates

Sae-Bae¹,

Memon²,

Sooraksa³

2018

Pattern Recognition

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This paper proposes three measures to quantify the characteristics of online signature templates in terms of distinctiveness, complexity and repeatability. A distinctiveness measure of a signature template is computed from a set of enrolled signature samples and a statistical assumption about random signatures. Secondly, a complexity measure of the template is derived from a set of enrolled signature samples. Finally, given a signature template, a measure to quantify the repeatability of the online signature is derived from a validation set of samples. These three measures can then be used as an indicator for the performance of the system in rejecting random forgery samples and skilled forgery samples and the performance of users in providing accepted genuine samples, respectively. The effectiveness of these three measures and their applications are demonstrated through experiments performed on three online signature datasets and one keystroke dynamics dataset using different verification algorithms.

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An adaptive bimodal recognition framework using sparse coding for face and ear

Cited by 30 publications

References 18 publications

Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images

Peer Review #2 of "Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images (v0.1)"

Distinctiveness, complexity, and repeatability of online signature templates

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