Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning

Author:

Norah Abdullah Al-johani 1,Elrefaei Lamiaa A.2ORCID

Affiliation:

1. Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

2. Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia & Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt

Abstract

Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Previous systems relied on handcrafted algorithms. The advancements of deep learning (DL) in the features learned by the convolutional neural network (CNN) has led to its application in PP and DHV recognition systems. In this article, a multimodal biometric system based on PP and DHV using (VGG16, VGG19 and AlexNet) CNN models is proposed. The proposed system is uses two approaches: feature level fusion (FLF) and Score level fusion (SLF). In the first approach, the features from PP and DHV are extracted with CNN models. These extracted features are then fused using serial or parallel fusion and used to train error-correcting output codes (ECOC) with a support vector machine (SVM) for classification. In the second approach, the fusion at score level is done with sum, max, and product methods by applying two strategies: Transfer learning that uses CNN models for features extraction and classification for PP and DHV, then score level fusion. For the second strategy, features are extracted with CNN models for PP and DHV and used to train ECOC with SVM for classification, then score level fusion. The system was tested using two DHV databases and one PP database. The multimodal system is tested two times by repeating PP database for each DHV database. The system achieved very high accuracy rate.

Publisher

IGI Global

Subject

General Medicine

Reference58 articles.

1. Al-johania, N. & Elrefaei, L. A. (2019). Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches. International Journal of Intelligent Engineering and Systems, 12(3), 178-191. doi: 10.22266/ijies2019.0630.19.

2. A multimodal deep learning framework using local feature representations for face recognition

3. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., . . . Awwal, A. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches.

4. Autonomous Institute of Government of Maharashtra. (2018). COEP Palm Print Database. Retrieved from www.coep.org.in/resources/coeppalmprintdatabase

5. Dorsal hand vein pattern analysis and neural networks for biometric authentication.;B.Belean;Studies in Informatics and Control,2017

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Dorsal Hand Vein Recognition System based on Various Machine and Deep Learning Classification Techniques;2023 3rd International Conference on Computing and Information Technology (ICCIT);2023-09-13

2. Improved Feature-Level Fusion-Based Biometric System for Genuine and Imposter Identification;International Journal of Information Security and Privacy;2022-08-04

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