Optimal feature selection-based biometric key management for identity management system: Emotion oriented facial biometric system

Padmanabhan, Suresh; Radhika, K. R.

doi:10.1016/j.jvcir.2020.103002

Cited by 4 publications

(1 citation statement)

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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 In sectors with a high-security level, the use of traditional key and smart card system has been replaced by the identification system of biometric technology.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%