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<p>In recent times, in modern smart city environments, securing and maintaining facial biometric security is crucial for preventing unauthorized access to citizen data and safeguarding it from spoofing. This research proposes a multimodal deep learning model with a cryptographic framework for securing facial biometric authentication. The multimodal system utilizes a convolutional neural network (CNN), the Residual Network (ResNet-50), and ElGamal cryptography to extract features from the face and secure the user’s facial information against spoofing attacks. The face biometric mappings are obtained from the first layer using a CNN with several convolutional layers, which retain spatial features like local texture, essential for the facial mapping function. After the first layer, the results are passed to a ResNet-50, which effectively identifies high-level semantic patterns using skip connections to predict accurate faces under varying pose and lighting conditions. Finally, the predicted face marking is secured using a cryptosystem, specifically the ElGamal cryptography technique, to ensure security and privacy when transmitting facial data within smart city networks. The proposed work utilized the CelebA Faces dataset to evaluate the model’s efficiency in secure data transmission within smart cities and to enhance security features that minimize authorization attacks. The facial mapping prediction result for the proposed model is 97.1%, with a low mean score loss of 0.04, indicating that the combined multimodal system performs well in a smart city environment. This performance shows that the proposed fused multimodal approach, which combines CNN low-level feature preservation, ResNet-50 high-level feature extraction, and ElGamal encryption, significantly outperforms the traditional model, such as CNN, with a higher accuracy percentage by 1.2%. ResNet-50 by 2.2%, Brakerski–Gentry–Vaikuntanathan algorithm by 1.1%, showing that the proposed deep learning model effectively handles spoofing and secure data transmission, making it a suitable solution for preventing unauthorized access in futuristic smart cities environments.</p>

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Secure facial biometric authentication in smart cities using multimodal methodology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Aanjankumar Sureshkumar,
  • Malathy Sathyamoorthy,
  • Rajesh Kumar Dhanaraj,
  • S. Aanjanadevi,
  • V. Palanisamy

摘要

In recent times, in modern smart city environments, securing and maintaining facial biometric security is crucial for preventing unauthorized access to citizen data and safeguarding it from spoofing. This research proposes a multimodal deep learning model with a cryptographic framework for securing facial biometric authentication. The multimodal system utilizes a convolutional neural network (CNN), the Residual Network (ResNet-50), and ElGamal cryptography to extract features from the face and secure the user’s facial information against spoofing attacks. The face biometric mappings are obtained from the first layer using a CNN with several convolutional layers, which retain spatial features like local texture, essential for the facial mapping function. After the first layer, the results are passed to a ResNet-50, which effectively identifies high-level semantic patterns using skip connections to predict accurate faces under varying pose and lighting conditions. Finally, the predicted face marking is secured using a cryptosystem, specifically the ElGamal cryptography technique, to ensure security and privacy when transmitting facial data within smart city networks. The proposed work utilized the CelebA Faces dataset to evaluate the model’s efficiency in secure data transmission within smart cities and to enhance security features that minimize authorization attacks. The facial mapping prediction result for the proposed model is 97.1%, with a low mean score loss of 0.04, indicating that the combined multimodal system performs well in a smart city environment. This performance shows that the proposed fused multimodal approach, which combines CNN low-level feature preservation, ResNet-50 high-level feature extraction, and ElGamal encryption, significantly outperforms the traditional model, such as CNN, with a higher accuracy percentage by 1.2%. ResNet-50 by 2.2%, Brakerski–Gentry–Vaikuntanathan algorithm by 1.1%, showing that the proposed deep learning model effectively handles spoofing and secure data transmission, making it a suitable solution for preventing unauthorized access in futuristic smart cities environments.