In　recent　years,　facial　recognition　technology　has　been　widely　adopted　in　modern　society.　However,　the　plaintext　storage,　computation,　and　transmission　of　facial　data　have　posed　significant　risks　of　information　leakage.　To　address　this　issue,　this　paper　proposes　a　facial　recognition　framework　based　on　approximate　homomorphic　encryption　(HE_FaceNet),　aimed　at　effectively　mitigating　privacy　leaks　during　the　facial　recognition　process.　The　framework　first　utilizes　a　pre-trained　model　to　extract　facial　feature　templates,　which　are　then　encrypted.　The　encrypted　templates　are　matched　using　Euclidean　distance,　with　the　final　recognition　being　performed　after　decryption.　However,　the　time-consuming　nature　of　homomorphic　encryption　calculations　limits　the　practical　applicability　of　the　HE_FaceNet　framework.　To　overcome　this　limitation,　this　paper　introduces　an　optimization　scheme　based　on　clustering　algorithms　to　accelerate　the　facial　recognition　process　within　the　HE_FaceNet　framework.　By　grouping　similar　faces　into　clusters　through　clustering　analysis,　the　efficiency　of　searching　encrypted　feature　values　is　significantly　improved.　Performance　analysis　indicates　that　the　HE_FaceNet　framework　successfully　protects　facial　data　privacy　while　maintaining　high　recognition　accuracy,　and　the　optimization　scheme　demonstrates　high　accuracy　and　significant　computational　efficiency　across　facial　datasets　of　varying　sizes.