Abstract
With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.
Highlights
IntroductionThe acceleration of the emergence of modern technological resources in recent years has given rise to a need for accurate user recognition systems in order to restrict access to the technologies
The acceleration of the emergence of modern technological resources in recent years has given rise to a need for accurate user recognition systems in order to restrict access to the technologies.The biometric recognition systems are the most powerful option to date
In this paper, a multimodal biometric model was developed for user identification
Summary
The acceleration of the emergence of modern technological resources in recent years has given rise to a need for accurate user recognition systems in order to restrict access to the technologies. The biometric recognition systems are the most powerful option to date. Biometrics is the science of establishing the identity of a person through semi- or fully-automated techniques based on behavioral traits, such as voice or signature, and/or physical traits, such as the iris and the fingerprint [1]. Biometric traits can be categorized into two groups: extrinsic biometric traits such as iris and fingerprint, and intrinsic biometric traits such as Sensors 2020, 20, 5523; doi:10.3390/s20195523 www.mdpi.com/journal/sensors. Extrinsic traits are visible and can be affected by external factors, while the intrinsic features cannot be affected by external factors [3]
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