Feature level fusion of Face and Iris using Deep Features based on Convolutional Neural Networks

D, Supreetha Gowda H; Imran, Mohammed; G, Hemantha Kumar

doi:10.1109/icacci.2018.8554683

Cited by 4 publications

(1 citation statement)

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“…Many of the shortcomings in traditional machine learning methods, particularly in feature extraction procedures, have been solved by deep learning algorithms. Deep neural networks can adapt to changes in biometric images and extract features from raw data [6,7]. In this article, the Sect.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Deep Learning Based Score Level Fusion Using Face and Fingerprint

Shinde¹,

Kayte²

2023

Advances in Intelligent Systems Research

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In the previous decade, biometrics referred to the automatic recognition of persons based on their physiological or behavioural traits but unimodal biometrics have their limitations. Due to its potential to overcome some of the inherent limitations of single biometric modalities while simultaneously enhancing overall recognition rates, multimodal biometrics has recently gained prominence. In this research, we offer a multimodal biometric person authentication system based on pre-train transfer learning VGG16 with CNN and CNN models, that uses the user's face and fingerprint biometric traits. We have used the own collected samples of same person KVKR face and fingerprint dataset for experimental work. First, we have applied pre-processing data augmentation technique on face and fingerprint data then image enhancement techniques on fingerprint data. In the features extraction, we have extraction the hidden feature of the face and fingerprint images using pre-train VGG16 with CNN and CNN models. The hstack method has been used to combine the features and SoftMax classifier use for features classification. The fusion score is calculated using the fixed-rule-based maximum rule technique, finally we have done comparative analysis of the unimodal and multimodal biometric recognition system.

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Section: Introductionmentioning

confidence: 99%