PCG signals for biometric authentication systems: An in-depth review

El‐Dahshan, El‐Sayed A.; Bassiouni, Mahmoud M.; Sharvia, Septavera; Salem, A.

doi:10.1016/j.cosrev.2021.100420

Cited by 3 publications

(1 citation statement)

References 82 publications

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“…Human biometrics are relatively stable, and there are no problems such as loss and forgetting. Compared with the traditional cryptographic key authentication method [4][5][6][7], it has more efective system security. Te biometric identifcation method uses the human body marking method, which is easy to carry with you and can realize real-time collection, identifcation, and judgment of biometric information.…”

Section: Introductionmentioning

confidence: 99%

Biometric Authentication and Correlation Analysis Based on CNN‐SRU Hybrid Neural Network Model

Zhang

Yang

2023

Computational Intelligence and Neuroscience

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With the continuous development of computer technology, many institutions in society have higher requirements for the efficiency and reliability of identification systems. In sectors with a high-security level, the use of traditional key and smart card system has been replaced by the identification system of biometric technology. The use of fingerprint and face recognition in biometric technology is a biometric technology that does not constitute an infringement on the human body and is convenient and reliable. The biometric technology has been continuously improved, and the existing biometric technologies are based on unimodal biometric features. The unimodal biometric technology has its own limitations such as proposing single information and checking data affected by the environment, which makes it difficult for the technology to play its advantages in practical applications. In this paper, we use CNN-SRU deep learning to preprocess a large amount of complex data in the perceptual layer. The data collected in the perceptual layer are first transmitted to CNN convolutional neural network for simple classification and analysis and then arrives at the LSTM session to update again and optimize the screening to improve the biometric performance. The results show that the CNN-LSTM, CNN-GRU, and CNN algorithms show a decreasing trend in accuracy under the three error evaluation criteria of RMSE, MAE, and ME, from 0.35 to 0.07, 0.58 to 0.19, and 0.38 to 0.15, respectively. The recognition rate of multifeature fusion can reach 95.2%; the recognition efficiency of the multibiometric authentication system and accuracy rate has been significantly improved. It provides a strong guarantee for the regional standardization, high integration, generalization, and modularization of multibiometric identification system application products.

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Section: Introductionmentioning

confidence: 99%

Biometric Authentication and Correlation Analysis Based on CNN‐SRU Hybrid Neural Network Model

Zhang

Yang

2023

Computational Intelligence and Neuroscience

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A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier

Chopparapu

Seventline

2023

AIP Advances

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Multi-modal classification plays a vital role for the real-time applications since most of the conventional models are independent of homogeneous features with single classifier. In addition, traditional directional gradient descriptors are difficult to find the heterogeneous features on different biometric classification. Feature extraction, segmentation, and multi-modal classification are the essential key factors that improve the true positive rate, error rate, and false positive rate of human-based recognition systems. In this work, a hybrid ensemble-based feature selection ranking measure, hybrid segmentation, and ensemble multi-class multi-modal classification framework on different biometric features is designed. Experimental results show that the proposed multi-class multi-modal ensemble classification framework has better optimization in terms of false positive rate, error rate, and precision than the conventional homogeneous local gradient feature extraction-based classification models on different features.

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Remote Optical Sensing of Heart Sounds for Biometric Identification Using Information Fusion

Li,

Vaskeviciute,

Starshynov

et al. 2024

IEEE Trans. Instrum. Meas.

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PCG signals for biometric authentication systems: An in-depth review

Cited by 3 publications

References 82 publications

Biometric Authentication and Correlation Analysis Based on CNN‐SRU Hybrid Neural Network Model

Biometric Authentication and Correlation Analysis Based on CNN‐SRU Hybrid Neural Network Model

A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier

Remote Optical Sensing of Heart Sounds for Biometric Identification Using Information Fusion

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