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

Norah Abdullah Al-johani, Lamiaa A. Elrefaei

Source Title: International Journal of Artificial Intelligence and Machine Learning (IJAIML)10(2)

ISSN: 2642-1577|EISSN: 2642-1585|EISBN13: 9781799809289|DOI: 10.4018/IJAIML.2020070102

MLA

Norah Abdullah Al-johani, and Lamiaa A. Elrefaei. "Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning." IJAIML vol.10, no.2 2020: pp.18-42. http://doi.org/10.4018/IJAIML.2020070102

APA

Norah Abdullah Al-johani & Elrefaei, L. A. (2020). Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 10(2), 18-42. http://doi.org/10.4018/IJAIML.2020070102

Chicago

Norah Abdullah Al-johani, and Lamiaa A. Elrefaei. "Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 10, no.2: 18-42. http://doi.org/10.4018/IJAIML.2020070102

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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.