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This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted feature vectors, we perform the following ciphertext operations, i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) scale normalization to unit ℓ_2-norm, and iv) match score computation. Our method, dubbed HEFT (Homomorphically Encrypted Fusion of biometricarXiv:2208.07241v1 fatcat:ss53ip4xxzhrrgylpn7echlvpe