Robust Deep Identification using ECG and Multimodal Biometrics for Industrial Internet of Things

Alkeem, Ebrahim Al; Yeun, Chan Yeob; Yun, Jae-Woong; Yoo, Paul D.; Chae, Myungsu; Rahman, Arafatur; Asyhari, A. Taufiq

doi:10.1016/j.adhoc.2021.102581

Cited by 37 publications

(16 citation statements)

References 47 publications

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“…The recallratio of the positive true samples and total of the positive true samples plus the false-negative true samples is known as measurement. TP stands for True Positive; FN stands for False Negative (10).…”

Section: Resultsmentioning

confidence: 99%

“…The paper [9] proposed three different models to improve heterogeneous (cross-sensor) iris identi cation based on the ensemble of convolutional and residual blocks. The paper [10] suggested a unique, robust, and trustworthy identi cation method based on multimodal biometrics, which combines ngerprint, ECG, and facial image data using deep learning. This method is especially effective for gender identi cation and identi cation.…”

Section: Related Workmentioning

confidence: 99%

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Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Amin,

Gantra

et al. 2024

Preprint

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Biometric identification technology has become a common part of daily life due to the global demand for information security and security legislation. Due to its capacity to circumvent several fundamental drawbacks of unimodal biometric systems, multimodal biometrics technology has attracted attention and grown in popularity in this respect. This research presents a novel multimodal biometric person identification system based on a VGG19 with softmax classifier (VGG19-SC) for iris and facial biometrics. The system's architecture is built on VGG19-SC, which extracts features from and categorizes images. The system was created by combining the iris and face portions of two VGG19-SC models. VGG-19 was employed to construct the well-known pertained model. A few methods, including picture augmentation and dropout techniques, were used to prevent overfitting. The VGG19-SC models were fused using feature-level and score-level fusion methods to investigate the effects of these fusion methods on recognition performance. The results demonstrated that three biometric features were more effective than two and one biometric traits in biometric identification systems. The findings similarly demonstrated the suggested method easily surpassed other cutting-edge approaches by obtaining an accuracy of 99.39% in a multi-biometric verification system.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Amin,

Gantra

et al. 2024

Preprint

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“…To improve prediction accuracy and robustness, multimodal biometric systems [7][8][9][10][11][12] combine many biometric modalities. Ranking Deep convolution neural networks (RDCNN) is used in this paper to construct a feature extraction model for noisy sensor data.…”

Section: Introductionmentioning

confidence: 99%

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

Vasavi¹,

Abirami²

2023

Intelligent Automation &Amp; Soft Computing

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In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model enables the feature extraction from different biometric images that includes iris, palm print, and lip, where the images are preprocessed initially for further processing. The extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the feature extraction model and then testing the model with different sets of input images. The simulation is performed in matlab to test the efficacy of the modal over multi-modal datasets and the simulation result shows that the proposed method achieves increased accuracy, precision, recall, and F1 score than the existing deep learning feature extraction methods. The performance improvement of the MBFE Algorithm technique in terms of accuracy, precision, recall, and F1 score is attained by 0.126%, 0.152%, 0.184%, and 0.38% with existing Back Propagation Neural Network (BPNN), Human Identification Using Wavelet Transform (HIUWT), Segmentation Methodology for Non-cooperative Recognition (SMNR), Daugman Iris Localization Algorithm (DILA) feature extraction techniques respectively.

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“…Such methods have intrinsic security limitations, as they can be affected by light or ambient noise, and can be easily “fooled” by artificial replicas (e.g., silicone finger replicas, audio recordings or pictures). Such caveats have driven researchers in the field to explore other alternatives [ 1 , 2 ]. ECG signals have demonstrated particularly advantageous properties, becoming a practical alternative for real-world deployment with modern approaches that addressed the usability constraints of this modality.…”

Section: Introductionmentioning

confidence: 99%

System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics

Melo

Neto

Silva

2022

Sensors

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Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily “fooled” by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today’s demanding security systems—mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC’s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270× speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.

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Robust Deep Identification using ECG and Multimodal Biometrics for Industrial Internet of Things

Cited by 37 publications

References 47 publications

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Improved Human Identification by Multi-biometric Image Sensor Integration With a Deep Learning Approach

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics

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