Although　the　KISSME　approach　can　effectively　reduce　the　correlation　between　feature　vectors　of　samples,　it　can＇t　restrain　the　multimodal　distribution　of　vectors　in　the　global　level.　This　drawback　brings　negative　impact　on　classification　performance.　Inspired　by　KISSME,　we　propose　our　fusion　algorithm　of　Likelihood　Ratio　Test　and　cosine　similarity　for　large　scale　face　verification　(CS-KISSME).　In　our　algorithm,　we　obtain　an　approximate　optimum　Mahalanobis　distance　matrix　and　use　it　as　a　projection　matrix.　After　that,　we　measure　the　dissimilarities　by　cosine　similarity　in　the　linear　transformed　subspace.　In　this　paper,　we　expound　merits　of　CS-KISSME　theoretically,　test　it　on　two　challenging　face　verification　datasets　and　achieve　higher　accuracy　and　better　robustness　with　little　time　consumption.