Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

Cherrat, El mehdi; Alaoui, Rachid; Bouzahir, Hassane

doi:10.11591/ijece.v9i4.pp2425-2432

Cited by 22 publications

(16 citation statements)

References 21 publications

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“…For pre-processing step, Soble and TopHat filtering method improved the quality of the image by limiting the contrast. After that, K-means and DBSCAN approaches are applied to classify the image into foreground and background region (Cherrat, Alaoui & Bouzahir, 2019). In addition, the Canny method (Canny, 1987) and the inner rectangle are adopted to extract the Region of interest (ROI) of fingerprint segmented.…”

Section: Proposed System Fingerprint Recognition Systemmentioning

confidence: 99%

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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

Self Cite

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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: Proposed System Fingerprint Recognition Systemmentioning

confidence: 99%

“…The accuracy rate for proposed systems and different recognition biometric system results.Enhanced fingerprint CNN usingCherrat, Alaoui & Bouzahir (2019) 99.48Enhanced fingervein CNN usingYing et al (2017) …”

mentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Proposed System Fingerprint Recognition Systemmentioning

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

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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 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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“…A centroid is the location representing the center of the cluster. K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Developing digital signal clustering method using local binary pattern histogram

Rasras

Zahran

Sara

et al. 2021

IJECE

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In this paper we presented a new approach to manipulate a digital signal in order to create a features array, which can be used as a signature to retrieve the signal. Each digital signal is associated with the local binary pattern (LBP) histogram; this histogram will be calculated based on LBP operator, then k-means clustering was used to generate the required features for each digital signal. The proposed method was implemented, tested and the obtained experimental results were analyzed. The results showed the flexibility and accuracy of the proposed method. Althoug different parameters of the digital signal were changed during implementation, the results obtained showed the robustness of the proposed method.

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Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

Cited by 22 publications

References 21 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)"

Developing digital signal clustering method using local binary pattern histogram

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