An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

Borra, Subba Reddy; Reddy, G. Jagadeeswar; Reddy, E. Sreenivasa

doi:10.11591/ijece.v8i2.pp1194-1213

Cited by 16 publications

(20 citation statements)

References 25 publications

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“…Moreover, the recognition accuracy is relatively very high to others biometric recognition system based on the retina, ear shape, iris, etc. (Borra, Reddy & Reddy, 2018).…”

Section: Introductionmentioning

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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“…Moreover, the recognition accuracy is relatively very high to others biometric recognition system based on the retina, ear shape, iris, etc. (Borra, Reddy & Reddy, 2018).…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…The most common biometric system is fingerprint recognition. It is considered an excellent biometric modality for identification or verification the person, especially in the latest smart phones and consumer devices [2,3].…”

Section: Introductionmentioning

confidence: 99%

A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

Cherrat

Alaoui

Bouzahir

2020

IJEECS

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<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

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“…Moreover, the recognition accuracy is relatively very high to others biometric recognition system based on the retina, ear shape, iris, etc. (Borra et al, 2018). The finger vein biometric modality is usually used in biometric recognition because of many advantages compared other modality, 1) it is simple and easy to use : easily acquired using sensor capable of capturing or the NIR (Near-Infrared) light source; 2) it is high security: the vein structure is hidden inside the skin and the possibility of spoof the human recognition system is very complex; 3) the veins of each individual are unique and different (Yang & Zhang, 2012).…”

Section: Introductionmentioning

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.

show abstract

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

Cited by 16 publications

References 25 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

A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein 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)"

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