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<p>Face recognition (FR) has emerged as a vital biometric authentication method due to its non-invasive nature and widespread applications in security, surveillance, and identity verification. However, existing FR systems face significant challenges in preserving user privacy, especially when deployed in cloud environments. Traditional approaches lack robust encryption mechanisms, making sensitive facial data vulnerable to breaches, adversarial attacks, and unauthorised access. This study introduces a novel privacy-preserving face recognition (PPFR) framework that leverages deep learning (DL) models, specifically FaceNet and ArcFace, for extracting discriminative facial embeddings. The framework combines these models with the Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme to enable secure computation on encrypted data without requiring decryption. Locality Preserving Projection (LPP) is applied to compress high-dimensional features while preserving the essential data structure, thereby enhancing performance and reducing processing overhead. The proposed system was evaluated on four benchmark datasets: CASIA3D, 105PinsFace, LFW, and Faces94. Among the models, ArcFace showed the best performance, achieving high accuracy and low Equal Error Rates (EER), with results of 99.30% accuracy on CASIA3D, 98.06% on 105PinsFace, 97.07% on LFW, and 99.82% on Faces94 datasets. The experimental results demonstrate that the proposed system is both effective and secure, making it suitable for real-world face recognition applications with enhanced privacy protection.</p>

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Locality preserving and homomorphically encrypted deep features for privacy-preserving face recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Pavani Chitrapu,
  • Hemantha Kumar Kalluri,
  • Mahesh Kumar Morampudi,
  • Satya Krishna Nunna

摘要

Face recognition (FR) has emerged as a vital biometric authentication method due to its non-invasive nature and widespread applications in security, surveillance, and identity verification. However, existing FR systems face significant challenges in preserving user privacy, especially when deployed in cloud environments. Traditional approaches lack robust encryption mechanisms, making sensitive facial data vulnerable to breaches, adversarial attacks, and unauthorised access. This study introduces a novel privacy-preserving face recognition (PPFR) framework that leverages deep learning (DL) models, specifically FaceNet and ArcFace, for extracting discriminative facial embeddings. The framework combines these models with the Cheon–Kim–Kim–Song (CKKS) homomorphic encryption scheme to enable secure computation on encrypted data without requiring decryption. Locality Preserving Projection (LPP) is applied to compress high-dimensional features while preserving the essential data structure, thereby enhancing performance and reducing processing overhead. The proposed system was evaluated on four benchmark datasets: CASIA3D, 105PinsFace, LFW, and Faces94. Among the models, ArcFace showed the best performance, achieving high accuracy and low Equal Error Rates (EER), with results of 99.30% accuracy on CASIA3D, 98.06% on 105PinsFace, 97.07% on LFW, and 99.82% on Faces94 datasets. The experimental results demonstrate that the proposed system is both effective and secure, making it suitable for real-world face recognition applications with enhanced privacy protection.