Face　recognition　technology　is　widely　used　in　various　fields,　such　as　law　enforcement,　payment　systems,　transportation,　and　access　control.　Traditional　face　authentication　systems　typically　establish　a　facial　feature　template　database　for　identity　verification.　However,　this　approach　poses　various　security　risks,　such　as　the　risk　of　plaintext　feature　data　stored　in　cloud　databases　being　leaked　or　stolen.　To　address　these　issues,　in　recent　years,　a　face　recognition　technology　based　on　homomorphic　encryption　has　gained　attention.　Based　on　homomorphic　encryption,　face　recognition　can　encrypt　facial　feature　values　and　achieve　feature　matching　without　exposing　the　feature　information.　However,　due　to　the　encryption,　face　recognition　in　the　ciphertext　domain　often　requires　considerable　time.　In　this　paper,　we　introduce　the　big　data　stream　processing　engine　Flink　to　achieve　parallel　computation　of　face　recognition　in　the　ciphertext　domain　based　on　homomorphic　encryption.　We　analyze　the　security,　accuracy,　and　acceleration　of　this　approach.　Ultimately,　we　verify　that　this　approach　achieves　recognition　accuracy　close　to　plaintext　and　significant　efficiency　improvement.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.