Research of dual-modal decision level fusion for fingerprint and finger vein image

Ma, Hui; Popoola, Oluwatoyin P.; Sun, Shuli

doi:10.1504/ijbm.2015.071949

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…An optimization method is introduced to improve the feature level fusion, which involves reducing the feature space by applying various methods. In 2015, Ma et al [40] proposed a new FV and fingerprint authentication system based on multi-route detection. This study introduces a new biometric identity authentication system that combines fingerprint and finger vein recognition using a multi-route approach.…”

Section: Related Workmentioning

confidence: 99%

Finger Vein Recognition Based on Multi-Features Fusion

Titrek¹,

Baykan²

2023

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Biometric Recognition Systems allow individuals to be automatically authenticated or identified by using their unique characteristics. Finger vein (FV), widely used for this purpose, has a crucial place among biometric systems because of its advantages, which are user-friendliness, ability to detect living tissue, high reliability, low system cost, and less area requirement in installation. It has a wide usage area, especially in places where personal safety is at the forefront. In this study, we examine the effect of the Horizontal and Vertical Total Proportion (HVTP) feature extraction algorithm on the success rate when the fusion technique is applied. Homomorphic Filter (HF) and Perona-Malik Anisotropic Diffusion (PMAD) are used to remove the noise and light scattering issue in the FV databases, and Gray Level Run Length Matrices (GLRLM), Gray Level Co-occurrence Matrices (GLCM), Segmentation-based Fractal Texture Analysis (SFTA), Horizontal Total Proportion (HTP), and Vertical Total Proportion (VTP) methods are applied to describe texture features. The fusion of multiple features instead of using only one type of feature can improve the accuracy of FV recognition systems. The novelty of the study is the fusion of HTP and VTP with the GLRLM, GLCM, and SFTA features by using Yang finger vein databases (Database_1) and MMCBNU_6000 (Database_2). Experimental results reveal that the HTP and VTP significantly improved the classification success in these FV image databases. The best success rate achieved in the Ensemble classifier is 99.7% using Database_1 and 97.6% using Database_2.

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Section: Related Workmentioning

confidence: 99%

Finger Vein Recognition Based on Multi-Features Fusion

Titrek¹,

Baykan²

2023

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“…The drawbacks of this system, it does not accord incompatible feature sets (e.g., eigen-coefficients of face and minutiae points of fingerprints) to be combined and it is difficult to predict the best fusion strategy given a scenario. A novel fingerprint and finger vein identification system by concatenating the feature vectors are achieved by Ma, Popoola & Sun (2015). In this study, the extracted feature vectors of both fingerprint and finger vein images are concatenated in order to combine the classifiers recognition results at the decision level.…”

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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“…The drawbacks of this system, it does not accord incompatible feature sets (for example eigen-coefficients of face and minutiae points of fingerprints) to be combined and it is difficult to predict the best fusion strategy given a scenario. A novel fingerprint and finger vein identification system by concatenating the feature vectors are achieved by Ma.al (Ma, Popoola & Sun, 2015). In this study, the extracted feature vectors of both fingerprint and finger vein images are concatenated in order to combine the classifiers recognition results at the decision level.…”

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

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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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Research of dual-modal decision level fusion for fingerprint and finger vein image

Cited by 6 publications

References 16 publications

Finger Vein Recognition Based on Multi-Features Fusion

Finger Vein Recognition Based on Multi-Features Fusion

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)"

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