Abstract
In the last decade, the biometrics refers to automatic recognition of persons using their physiological or behavioral characteristics. The combination of multiple biometrics or, multimodal biometrics have higher accuracy to verify the person and ensure that its information or data is safer compared to system based on single biometrics modality. In this regard, this paper introduces a scheme for multimodal biometric recognition system based on the fusion of finger-vein and face images using Convolutional Neural Network (CNN) and different classifiers. The pre-processed finger-vein image using Adaptive Histogram Equalization (AHE) is input into a CNN model. Then, Random Forest (RF) classifier performs as a recognizer. In addition, a hybrid CNN-Linear Support Vector Machine (SVM) model is used for recognizing face images. After this process, the score level fusion of bimodal biometric based on the weighted concatenation is applied to identify the identity of the individual. Experimental results on publicly available VERA Fingervein database, Color Feret and Ar face database have shown significant capability of identification biometric system. The proposed system provides high recognition accuracy rate by 99,98% compared with other classical methods and traditional techniques based on normal recognition or CNN architectures.
Highlights
At present, recognition biometric system has been widely used in important fields such as criminal identification, securing access to buildings or personal objects, financial payment, etc
In order to better verify our algorithm, the following classification methods are adopted in the experiment: Support Vector Machine (SVM) [23], Random Forest (RF) [20], Logistic Regression (LR) [27] and Linear Discriminant Analysis (LDA) [28]
This paper presents a multimodal biometric identification system using Convolutional Neural Network (CNN) to fuse the finger vein and the face based on score level of fusion
Summary
Recognition biometric system has been widely used in important fields such as criminal identification, securing access to buildings or personal objects, financial payment, etc. Different fusion techniques and normalization methods of fingerprint, hand geometry and face biometric sources are achieved by Jain, et al [11] Another multimodal biometric system based on multi-instance iris recognition system using a fusion of right iris and left iris for the same individual is studied by Wang et al [12]. The researchers must experimentally decide on an optimal and effective algorithm for all stages in order to increase the biometric recognition accuracy To address this problem, the proposed method deploys the multimodal biometric recognition system that combines the finger-vein and face images using Convolutional Neural Network (CNN) architectures and classifiers based on Random Forest (RF) and Linear Support Vector Machine (SVM).
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