The　robustness　of　the　traditional　face　recognition　methods　is　not　strong,　and　the　instantaneity　and　precision　of　the　traditional　face　retrieval　methods　are　not　high.　In　order　to　dissolve　these　problems,　a　novel　face　image　recognition　and　retrieval　method　based　on　feedback　log　information　is　presented.　It　first　extracts　the　feature　vectors　by　SIFT　(Scale　Invariant　Feature　Transform).　These　extracted　features　are　invariant　to　illumination,　rotation,　translation,　scale　and　expression.　Then　eliminates　the　false　matching　points　according　to　the　improved　SIFT　feature　vectors　matching　criteria,　calculates　the　distance　between　the　face　images　with　the　residual　matching　points,　and　completes　the　first　retrieve,　with　the　result　of　returning　the　top　50　similar　images.　After　this,　refines　the　first　result　by　relevance　feedback　strategy　of　the　feedback　log　information　to　solve　the　semantic　gap　between　the　high-level　semantics　and　low-level　features　of　images.　The　experimental　results　show　that,　the　proposed　method　has　higher　retrieval　accuracy　and　more　fast　retrieval　speed,　and　it　is　more　suitable　for　real-time　video　investigation　applications　than　the　traditional　relevance　feedback.　©　2012　IEEE.