Affine　Scale　Invariant　Feature　Transform　(ASIFT)　is　robust　to　scales,　rotation,　scaling　and　affine　transformation.　It　could　be　used　for　face　recognition　with　pose　variation.　However,　ASIFT　requires　large　data.　Could　we　reduce　the　data　of　ASIFT　and　preserve　the　face　recognition　performance?　In　this　paper,　we　propose　an　effective　face　recognition　algorithm　to　combining　the　structural　similarity　(SSIM)　and　PCA-ASIFT　(PCA-ASIFT&SSIM).　First,　we　reduce　ASIFT　dimension　using　principal　component　analysis　and　get　PCA-ASIFT.　The　PCA-ASIFT＇s　discriminative　capability　drops　because　of　the　dimension　reduction.　It　brings　about　more　false　SIFT　matching.　We　further　introduce　the　SSIM　to　reduce　the　false　matching.　The　experimental　results　show　the　efficiency　of　the　proposed　approach.